Gallatin

Gallatin AI · Supports Organization · name

“Gallatin AI”

Black Flag · Supports Organization

“Gallatin builds AI-powered defense logistics software spanning demand forecasting, inventory optimization, and asset visibility.”

Gallatin AI · Sep 15, 2026 · Supports Organization technology, System

“Gallatin AI deployed its AI/ML sustainment decision-support application, Navigator, in support of the U.S. Army's 11th Airborne Division during Exercise Yama Sakura 91.”

PR Newswire · Jun 11, 2026 · Supports Facility, Organization · founded year, Organization · hq city, Role, System

“Headquartered in El Segundo, CA with offices in Washington, DC and Austin, TX”

Gallatin · Supports System

“Phalanx combines traditional reporting with the latest hardware sensors and computer vision solutions for in-transit visibility and inventory management at the point of need. Phalanx delivers the ground truth of asset status, saving precious time and exponentially increasing data accuracy.”

FreightWaves · Apr 8, 2025 · Supports Funding round · amount, Funding round · stage

“In addition to 8VC, the seed round included participation from Silent Ventures, Moonshots Capital, Timeless Partners and Banter Capital.”

Gallatin AI · Apr 8, 2025 · Supports Funding round · amount, Funding round · investors, Role

“today emerged from stealth with $15 million in seed funding.”

Gallatin AI · Feb 27, 2026 · Supports Facility

“If you're an engineer who wants to build software that military logisticians will actually use, we're hiring in Austin.”

Gallatin AI · Aug 12, 2025 · Supports Role, Supply relationship

“said Daniel Buchmueller, CTO of Gallatin.”

Gallatin AI · Mar 11, 2026 · Supports Role

“said Woody Glier, CEO of Gallatin AI.”

Linkedin via Bright Data · Supports Organization · company size, Organization · logo, Organization · url, Organization metric · employees in linkedin, Organization metric · followers

“{"company_size": "11-50 employees"}”

LinkedIn via Bright Data · Supports Job posting · job listing

“{"company_id": "106410794", "company_name": "Gallatin AI, Inc.", "company_url": "https://www.linkedin.com/company/gallatinai?trk=public_jobs_topcard-org-name", "job_employment_type": "Full-time", "job_location": "El Segundo, CA", "job_posted_time": "11 months ago", "job_posting_id": "4318503729", "job_seniority_level": "Entry level", "job_summary": "About Gallatin At Gallatin, we are rebuilding logistics infrastructure for the national security missions of the United States and allied partners. We build AI systems that determine how logistics decisions are made — not just how they're executed. From factory to foxhole, we operate at the layer where data becomes decisions, and decisions make the advantage. About The Role We’re seeking a Backend Software Engineer to build, maintain, and scale the core systems that support our AI-driven logistics platform. In this foundational role, you’ll be responsible for creating reliable and performant backend services, APIs, and data processing pipelines critical to the success of our products. What You'll Do Backend Development & System Design Develop robust, scalable backend services and APIs powering our logistics solutions. Design and maintain system architecture, ensuring performance, scalability, and security. Ensure high service availability and reliability through proactive monitoring, logging, and alerting. Implement and maintain robust testing strategies including unit, integration, and performance testing to ensure platform stability and reliability. Collaborate closely with front-end, AI/ML, and product teams to integrate services seamlessly. Data Processing & Integration Implement data ingestion pipelines from structured, unstructured, real-time, and batch data sources. Maintain data consistency, reliability, and availability through effective database schema design and optimization. Collaboration & Continuous Improvement Participate in code reviews and contribute to coding standards, best practices, and documentation. Stay informed of backend development trends and introduce relevant improvements to our technology stack. Support onboarding and mentoring of new team members as we grow. What We’re Looking For Strong Technical Background Proficiency in one or more backend languages: Python, Node.js, Typescript or Java Solid understanding of API design and implementation (REST, GraphQL). Hands-on experience with relational and NoSQL databases such as PostgreSQL, MongoDB, DynamoDB, or Redis. Cloud & Infrastructure Experience Familiarity with cloud environments (AWS, GCP, Azure). Experience deploying and managing containerized applications (Docker, Kubernetes). Understanding of infrastructure as code (Terraform, CloudFormation). Software Best Practices Experience with software testing methodologies (unit, integration, end-to-end testing). Commitment to writing clean, maintainable, well-documented code. Problem-solving & Communication Excellent problem-solving skills with the ability to debug complex systems. Strong collaboration skills, capable of working effectively across teams and clearly communicating technical concepts. Growth Mindset Passion for continuous learning and staying current with the latest advancements in backend engineering, distributed systems, and cloud infrastructure, as well as a commitment to mentoring and teaching others. Bonus Points Experience with building and integrating on Palantir Foundry and its various offerings Knowledge of defense or logistics industries and legacy system modernization. Contributions to open-source software or active engagement with the developer community. Mission and Identity We are building the system that enables faster, smarter logistics decisions in contested environments, and we're doing it with a team of seasoned entrepreneurs, operators, and technologists who have built and scaled solutions in this space before. We hold ourselves to an extremely high standard. We value clear thinking, direct communication, and the kind of ownership that doesn't stop until something actually works. Our mission is to create decision advantage when the stakes are the highest. If we succeed, the system doesn't just run; it gets smarter. We're not building AI for its own sake. We're building it because faster, smarter decisions in the most demanding environments on earth can't wait. If you want to work somewhere the stakes are real and the mission is urgent — you'll fit in here. Why Gallatin? The logistics infrastructure that supports America's warfighters and humanitarian disaster responders is overdue for transformation, and we are building it. From defense operations to disaster response, we're solving the hardest problems that keep missions moving when it matters most. Join a team where the mission is the point. Compensation: Gallatin offers competitive compensation commensurate with experience. Actual compensation may vary based on experience, skills, and location. In addition to base salary, we offer a generous equity grant, full healthcare coverage, 401k, unlimited PTO, and the perks of working in a high-caliber, mission-driven environment. Gallatin is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, sex, national origin, age, disability, veteran status, sexual orientation, gender identity, or any other characteristic protected by applicable federal, state, or local law. This position may require the ability to obtain and maintain a U.S. government security clearance. The successful candidate must be able to work in a classified environment when necessary. We comply with the United States Department of Labor's Pay Transparency provision . Note: Due to the nature of certain government contracts held by this organization, U.S. citizenship is a requirement for all positions at Gallatin. Proof of citizenship will be required prior to employment if selected. Compensation Range: $80K - $210K Show more Show less", "job_title": "Backend Engineer", "timestamp": "2026-10-05T04:47:55.209Z"}”

LinkedIn via Bright Data · Supports Job posting · job listing

“{"company_id": "106410794", "company_name": "Gallatin AI, Inc.", "company_url": "https://www.linkedin.com/company/gallatinai?trk=public_jobs_topcard-org-name", "job_employment_type": "Full-time", "job_location": "Austin, TX", "job_posted_time": "1 month ago", "job_posting_id": "4456790746", "job_seniority_level": "Mid-Senior level", "job_summary": "About Gallatin At Gallatin, we are rebuilding logistics infrastructure for the national security missions of the United States and allied partners. We build AI systems that determine how logistics decisions are made — not just how they're executed. From factory to foxhole, we operate at the layer where data becomes decisions, and decisions make the advantage. About The Role We're looking for a Senior Product Designer who thrives at the intersection of complex systems, AI, and craft. You'll research, prototype, and ship products that help military logisticians synthesize constraints and act within large-scale environments, both simulated and physical. Embedded within the Product team and reporting to the Director of Product Design, you'll blend human-factors rigor with rapid AI-assisted prototyping to increase the teamʼs learning velocity. This is a role for someone who nerds out over information density, emergent system behavior, and the design challenge of making complexity feel approachable. What You'll Do Collaborate with Product Managers and Engineers to develop user interfaces and experiences that serve the unique needs of military logistics operations Champion emerging best practices around AI prototyping to iterate at the speed of thought Conduct user research and testing with military stakeholders to validate design decisions Support the maturation of design systems that maintain consistency across our brand and products Participate in design reviews and contribute to product strategy discussions Document design decisions and maintain design specifications What We’re Looking For US Citizenship (required for working with defense-related projects) Bachelor's degree in Design, Human-Computer Interaction, or related field 5 years of product design experience, with demonstrated work on complex, data-rich, or systems-oriented products Background in or exposure to defense, intelligence, or government technology Fluency with design tools including Figma, Adobe Creative Suite, and modern design/prototyping tools Ability to think in systems: you consider states, edges, flows, and failure modes A portfolio that shows you can design for complexity (not just simplify it away) Excellent visual design skills with attention to detail Strong communication skills and ability to articulate design decisions Willingness to travel occasionally for user research and client meetings Bonus Points Experience designing for simulation, modeling, logistics, or other domains where users reason about dynamic systems over time Familiarity with front-end development (enough to prototype in code or pair fluently with engineers) Knowledge of accessibility standards, particularly for information-dense interfaces Mission and Identity We are building the system that enables faster, smarter logistics decisions in contested environments, and we're doing it with a team of seasoned entrepreneurs, operators, and technologists who have built and scaled solutions in this space before. We hold ourselves to an extremely high standard. We value clear thinking, direct communication, and the kind of ownership that doesn't stop until something actually works. Our mission is to create decision advantage when the stakes are the highest. If we succeed, the system doesn't just run; it gets smarter. We're not building AI for its own sake. We're building it because faster, smarter decisions in the most demanding environments on earth can't wait. If you want to work somewhere the stakes are real and the mission is urgent — you'll fit in here. Why Gallatin? The logistics infrastructure that supports America's warfighters and humanitarian disaster responders is overdue for transformation, and we are building it. From defense operations to disaster response, we're solving the hardest problems that keep missions moving when it matters most. Join a team where the mission is the point. Compensation: Gallatin offers competitive compensation commensurate with experience. Actual compensation may vary based on experience, skills, and location. In addition to base salary, we offer a generous equity grant, full healthcare coverage, 401k, unlimited PTO, and the perks of working in a high-caliber, mission-driven environment. Gallatin is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, sex, national origin, age, disability, veteran status, sexual orientation, gender identity, or any other characteristic protected by applicable federal, state, or local law. This position may require the ability to obtain and maintain a U.S. government security clearance. The successful candidate must be able to work in a classified environment when necessary. We comply with the United States Department of Labor's Pay Transparency provision . Show more Show less", "job_title": "Senior Product Designer", "timestamp": "2026-10-05T04:47:52.046Z"}”

LinkedIn via Bright Data · Supports Job posting · job listing

“{"company_id": "106410794", "company_name": "Gallatin AI, Inc.", "company_url": "https://www.linkedin.com/company/gallatinai?trk=public_jobs_topcard-org-name", "job_employment_type": "Full-time", "job_location": "San Francisco, CA", "job_posted_time": "2 weeks ago", "job_posting_id": "4466620829", "job_seniority_level": "Mid-Senior level", "job_summary": "About Gallatin At Gallatin, we are rebuilding logistics infrastructure for the national security missions of the United States and allied partners. We build AI systems that determine how logistics decisions are made — not just how they're executed. From factory to foxhole, we operate at the layer where data becomes decisions, and decisions make the advantage. What You'll Do In this role, you will build the systems that move a model out of a notebook and into the hands of a planner. Sometimes, that means deploying to an air-gapped rack in a tent instead of a VPC. You will own the path from training run to deployed capability: the infrastructure it runs on, the release process that ships it, the evaluation harness that proves it works, and the telemetry that tells us when it stops working. Our AI/ML team works across retrieval-grounded systems for doctrine and logistics data, document and feature extraction, military symbol recognition, optimization and movement models, and an LLM agent platform. This role underpins that work: building the infrastructure, release processes, and evaluation systems that make it shippable and keep it honest in production. You will have plenty of room to shape how we build it. Training & Serving Infrastructure Own model training, fine-tuning, and batch inference infrastructure across AWS (SageMaker, EKS) and on-premises GPU hardware. Stand up and tune LLM inference serving: vLLM-class stacks, quantization, continuous batching, KV-cache and throughput sizing. Make the hosted-vs-local call with numbers behind it. Build for DDIL: local inference with configurable fallback, degraded-mode behavior, and sane resource envelopes on hardware we do not get to choose. Release & Reproducibility Build CI/CD for models and pipelines: versioned datasets, a model registry, promotion gates, and rollback that actually works under pressure. Own infrastructure as code, containerization, and GitOps deployment across environments ranging from a dev cluster to a disconnected enclave. Make reproducibility a hard requirement. Any result we put in front of a customer or evaluator must be reproducible from a commit and dataset version. Evaluation & Observability Build and own the evaluation harness: regression suites for retrieval and extraction pipelines, LLM-as-judge pipelines with measured judge-human agreement, and adversarial and held-out sets. Instrument production for drift, latency, cost, retrieval quality, and failure modes, including quiet ones such as a retrieval miss that produces a fluent but wrong answer. Make our metrics defensible to external test and evaluation reviewers. “We think it’s good” is not a deliverable. Data & Pipeline Ownership Own ingestion, versioning, and lineage for logistics and doctrinal data drawn from a heterogeneous set of authoritative sources. Build and operate embedding and feature pipelines, incremental indexing, and the unglamorous systems that keep a retrieval index fresh. Build the human-in-the-loop infrastructure: confidence-scored routing, review queues, and feedback capture that improves the next model. Secure & Accredited Deployment Deploy and operate ML systems in IL5 and IL6 environments, including air-gapped or restricted-network enclaves. Build the release, observability, artifact-management, and incident-response workflows those environments require. Support ATO and continuous-authorization work with implementation evidence tied to applicable security controls (NIST SP 800-171, NIST SP 800-53 Rev. 5, CMMC Level 2, FIPS 140-3, and RMF/eMASS). Handle CUI and classified data correctly without being asked twice. What We’re Looking For Strong Platform & Infrastructure Skills 5+ years in MLOps, ML platform, or infrastructure engineering, including meaningful time working on systems with real users. Strong Python skills and comfort in a production codebase, not just notebooks. Deep Kubernetes and containerization experience, plus infrastructure as code. Production experience with AWS ML/Azure infrastructure (SageMaker, EKS, or equivalent). Hands-on GPU infrastructure experience: scheduling, utilization, memory sizing, and cost. Hands-on experience deploying and operating production software in IL5 or IL6 environments, including disconnected or restricted-network deployments. Production ML Judgment You have shipped an LLM or ML system to production and then had to keep it working. You know what breaks. You have built evaluation and monitoring for ML systems rather than adopting a vendor dashboard and hoping. You can reason about where a pipeline’s quality actually comes from and say so when a metric is measuring the wrong thing. Ownership You are comfortable with ambiguity and owning a domain end to end. This is a small team; there is no one to hand the pager to. You are willing to learn the mission domain. The engineers who do best here become genuinely interested in the logistics problem itself. Nice to Have Clearance: Preferred LLM serving and inference optimization (vLLM, TensorRT-LLM, quantization, prefix caching). Retrieval-grounded systems in production: hybrid retrieval, re-ranking, index freshness, and citation quality. Edge, on-premises, or disconnected deployment. Experience supporting ATO, continuous authorization, or production operations in classified environments. Defense, intelligence, or another accredited or regulated environment. Palantir Foundry, PostgreSQL/pgvector, NATS/JetStream, or ArgoCD. A degree in CS, engineering, or a related technical field, or the equivalent built the hard way. Mission and Identity We are building the system that enables faster, smarter logistics decisions in contested environments, and we're doing it with a team of seasoned entrepreneurs, operators, and technologists who have built and scaled solutions in this space before. We hold ourselves to an extremely high standard. We value clear thinking, direct communication, and the kind of ownership that doesn't stop until something actually works. Our mission is to create decision advantage when the stakes are the highest. If we succeed, the system doesn't just run; it gets smarter. We're not building AI for its own sake. We're building it because faster, smarter decisions in the most demanding environments on earth can't wait. If you want to work somewhere the stakes are real and the mission is urgent — you'll fit in here. Why Gallatin? The logistics infrastructure that supports America's warfighters and humanitarian disaster responders is overdue for transformation, and we are building it. From defense operations to disaster response, we're solving the hardest problems that keep missions moving when it matters most. Join a team where the mission is the point. Compensation: Gallatin offers competitive compensation commensurate with experience. Actual compensation may vary based on experience, skills, and location. In addition to base salary, we offer a generous equity grant, full healthcare coverage, 401k, unlimited PTO, and the perks of working in a high-caliber, mission-driven environment. Gallatin is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, sex, national origin, age, disability, veteran status, sexual orientation, gender identity, or any other characteristic protected by applicable federal, state, or local law. This position may require the ability to obtain and maintain a U.S. government security clearance. The successful candidate must be able to work in a classified environment when necessary. We comply with the United States Department of Labor's Pay Transparency provision . Note: Due to the nature of certain government contracts held by this organization, U.S. citizenship is a requirement for all positions at Gallatin. Proof of citizenship will be required prior to employment if selected. Compensation Range: $80K - $210K Show more Show less", "job_title": "Machine Learning Operations (MLOps) Engineer", "timestamp": "2026-10-05T04:47:54.444Z"}”

LinkedIn via Bright Data · Supports Job posting · job listing

“{"company_id": "106410794", "company_name": "Gallatin AI, Inc.", "company_url": "https://www.linkedin.com/company/gallatinai?trk=public_jobs_topcard-org-name", "job_employment_type": "Full-time", "job_location": "El Segundo, CA", "job_posted_time": "2 weeks ago", "job_posting_id": "4466625770", "job_seniority_level": "Mid-Senior level", "job_summary": "About Gallatin At Gallatin, we are rebuilding logistics infrastructure for the national security missions of the United States and allied partners. We build AI systems that determine how logistics decisions are made — not just how they're executed. From factory to foxhole, we operate at the layer where data becomes decisions, and decisions make the advantage. What You'll Do In this role, you will build the systems that move a model out of a notebook and into the hands of a planner. Sometimes, that means deploying to an air-gapped rack in a tent instead of a VPC. You will own the path from training run to deployed capability: the infrastructure it runs on, the release process that ships it, the evaluation harness that proves it works, and the telemetry that tells us when it stops working. Our AI/ML team works across retrieval-grounded systems for doctrine and logistics data, document and feature extraction, military symbol recognition, optimization and movement models, and an LLM agent platform. This role underpins that work: building the infrastructure, release processes, and evaluation systems that make it shippable and keep it honest in production. You will have plenty of room to shape how we build it. Training & Serving Infrastructure Own model training, fine-tuning, and batch inference infrastructure across AWS (SageMaker, EKS) and on-premises GPU hardware. Stand up and tune LLM inference serving: vLLM-class stacks, quantization, continuous batching, KV-cache and throughput sizing. Make the hosted-vs-local call with numbers behind it. Build for DDIL: local inference with configurable fallback, degraded-mode behavior, and sane resource envelopes on hardware we do not get to choose. Release & Reproducibility Build CI/CD for models and pipelines: versioned datasets, a model registry, promotion gates, and rollback that actually works under pressure. Own infrastructure as code, containerization, and GitOps deployment across environments ranging from a dev cluster to a disconnected enclave. Make reproducibility a hard requirement. Any result we put in front of a customer or evaluator must be reproducible from a commit and dataset version. Evaluation & Observability Build and own the evaluation harness: regression suites for retrieval and extraction pipelines, LLM-as-judge pipelines with measured judge-human agreement, and adversarial and held-out sets. Instrument production for drift, latency, cost, retrieval quality, and failure modes, including quiet ones such as a retrieval miss that produces a fluent but wrong answer. Make our metrics defensible to external test and evaluation reviewers. “We think it’s good” is not a deliverable. Data & Pipeline Ownership Own ingestion, versioning, and lineage for logistics and doctrinal data drawn from a heterogeneous set of authoritative sources. Build and operate embedding and feature pipelines, incremental indexing, and the unglamorous systems that keep a retrieval index fresh. Build the human-in-the-loop infrastructure: confidence-scored routing, review queues, and feedback capture that improves the next model. Secure & Accredited Deployment Deploy and operate ML systems in IL5 and IL6 environments, including air-gapped or restricted-network enclaves. Build the release, observability, artifact-management, and incident-response workflows those environments require. Support ATO and continuous-authorization work with implementation evidence tied to applicable security controls (NIST SP 800-171, NIST SP 800-53 Rev. 5, CMMC Level 2, FIPS 140-3, and RMF/eMASS). Handle CUI and classified data correctly without being asked twice. What We’re Looking For Strong Platform & Infrastructure Skills 5+ years in MLOps, ML platform, or infrastructure engineering, including meaningful time working on systems with real users. Strong Python skills and comfort in a production codebase, not just notebooks. Deep Kubernetes and containerization experience, plus infrastructure as code. Production experience with AWS ML/Azure infrastructure (SageMaker, EKS, or equivalent). Hands-on GPU infrastructure experience: scheduling, utilization, memory sizing, and cost. Hands-on experience deploying and operating production software in IL5 or IL6 environments, including disconnected or restricted-network deployments. Production ML Judgment You have shipped an LLM or ML system to production and then had to keep it working. You know what breaks. You have built evaluation and monitoring for ML systems rather than adopting a vendor dashboard and hoping. You can reason about where a pipeline’s quality actually comes from and say so when a metric is measuring the wrong thing. Ownership You are comfortable with ambiguity and owning a domain end to end. This is a small team; there is no one to hand the pager to. You are willing to learn the mission domain. The engineers who do best here become genuinely interested in the logistics problem itself. Nice to Have Clearance: Preferred LLM serving and inference optimization (vLLM, TensorRT-LLM, quantization, prefix caching). Retrieval-grounded systems in production: hybrid retrieval, re-ranking, index freshness, and citation quality. Edge, on-premises, or disconnected deployment. Experience supporting ATO, continuous authorization, or production operations in classified environments. Defense, intelligence, or another accredited or regulated environment. Palantir Foundry, PostgreSQL/pgvector, NATS/JetStream, or ArgoCD. A degree in CS, engineering, or a related technical field, or the equivalent built the hard way. Mission and Identity We are building the system that enables faster, smarter logistics decisions in contested environments, and we're doing it with a team of seasoned entrepreneurs, operators, and technologists who have built and scaled solutions in this space before. We hold ourselves to an extremely high standard. We value clear thinking, direct communication, and the kind of ownership that doesn't stop until something actually works. Our mission is to create decision advantage when the stakes are the highest. If we succeed, the system doesn't just run; it gets smarter. We're not building AI for its own sake. We're building it because faster, smarter decisions in the most demanding environments on earth can't wait. If you want to work somewhere the stakes are real and the mission is urgent — you'll fit in here. Why Gallatin? The logistics infrastructure that supports America's warfighters and humanitarian disaster responders is overdue for transformation, and we are building it. From defense operations to disaster response, we're solving the hardest problems that keep missions moving when it matters most. Join a team where the mission is the point. Compensation: Gallatin offers competitive compensation commensurate with experience. Actual compensation may vary based on experience, skills, and location. In addition to base salary, we offer a generous equity grant, full healthcare coverage, 401k, unlimited PTO, and the perks of working in a high-caliber, mission-driven environment. Gallatin is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, sex, national origin, age, disability, veteran status, sexual orientation, gender identity, or any other characteristic protected by applicable federal, state, or local law. This position may require the ability to obtain and maintain a U.S. government security clearance. The successful candidate must be able to work in a classified environment when necessary. We comply with the United States Department of Labor's Pay Transparency provision . Note: Due to the nature of certain government contracts held by this organization, U.S. citizenship is a requirement for all positions at Gallatin. Proof of citizenship will be required prior to employment if selected. Compensation Range: $80K - $210K Show more Show less", "job_title": "Machine Learning Operations (MLOps) Engineer", "timestamp": "2026-10-05T04:47:53.632Z"}”

LinkedIn via Bright Data · Supports Job posting · job listing

“{"company_id": "106410794", "company_name": "Gallatin AI, Inc.", "company_url": "https://www.linkedin.com/company/gallatinai?trk=public_jobs_topcard-org-name", "job_employment_type": "Full-time", "job_location": "Austin, TX", "job_posted_time": "2 weeks ago", "job_posting_id": "4466632628", "job_seniority_level": "Mid-Senior level", "job_summary": "About Gallatin At Gallatin, we are rebuilding logistics infrastructure for the national security missions of the United States and allied partners. We build AI systems that determine how logistics decisions are made — not just how they're executed. From factory to foxhole, we operate at the layer where data becomes decisions, and decisions make the advantage. What You'll Do In this role, you will build the systems that move a model out of a notebook and into the hands of a planner. Sometimes, that means deploying to an air-gapped rack in a tent instead of a VPC. You will own the path from training run to deployed capability: the infrastructure it runs on, the release process that ships it, the evaluation harness that proves it works, and the telemetry that tells us when it stops working. Our AI/ML team works across retrieval-grounded systems for doctrine and logistics data, document and feature extraction, military symbol recognition, optimization and movement models, and an LLM agent platform. This role underpins that work: building the infrastructure, release processes, and evaluation systems that make it shippable and keep it honest in production. You will have plenty of room to shape how we build it. Training & Serving Infrastructure Own model training, fine-tuning, and batch inference infrastructure across AWS (SageMaker, EKS) and on-premises GPU hardware. Stand up and tune LLM inference serving: vLLM-class stacks, quantization, continuous batching, KV-cache and throughput sizing. Make the hosted-vs-local call with numbers behind it. Build for DDIL: local inference with configurable fallback, degraded-mode behavior, and sane resource envelopes on hardware we do not get to choose. Release & Reproducibility Build CI/CD for models and pipelines: versioned datasets, a model registry, promotion gates, and rollback that actually works under pressure. Own infrastructure as code, containerization, and GitOps deployment across environments ranging from a dev cluster to a disconnected enclave. Make reproducibility a hard requirement. Any result we put in front of a customer or evaluator must be reproducible from a commit and dataset version. Evaluation & Observability Build and own the evaluation harness: regression suites for retrieval and extraction pipelines, LLM-as-judge pipelines with measured judge-human agreement, and adversarial and held-out sets. Instrument production for drift, latency, cost, retrieval quality, and failure modes, including quiet ones such as a retrieval miss that produces a fluent but wrong answer. Make our metrics defensible to external test and evaluation reviewers. “We think it’s good” is not a deliverable. Data & Pipeline Ownership Own ingestion, versioning, and lineage for logistics and doctrinal data drawn from a heterogeneous set of authoritative sources. Build and operate embedding and feature pipelines, incremental indexing, and the unglamorous systems that keep a retrieval index fresh. Build the human-in-the-loop infrastructure: confidence-scored routing, review queues, and feedback capture that improves the next model. Secure & Accredited Deployment Deploy and operate ML systems in IL5 and IL6 environments, including air-gapped or restricted-network enclaves. Build the release, observability, artifact-management, and incident-response workflows those environments require. Support ATO and continuous-authorization work with implementation evidence tied to applicable security controls (NIST SP 800-171, NIST SP 800-53 Rev. 5, CMMC Level 2, FIPS 140-3, and RMF/eMASS). Handle CUI and classified data correctly without being asked twice. What We’re Looking For Strong Platform & Infrastructure Skills 5+ years in MLOps, ML platform, or infrastructure engineering, including meaningful time working on systems with real users. Strong Python skills and comfort in a production codebase, not just notebooks. Deep Kubernetes and containerization experience, plus infrastructure as code. Production experience with AWS ML/Azure infrastructure (SageMaker, EKS, or equivalent). Hands-on GPU infrastructure experience: scheduling, utilization, memory sizing, and cost. Hands-on experience deploying and operating production software in IL5 or IL6 environments, including disconnected or restricted-network deployments. Production ML Judgment You have shipped an LLM or ML system to production and then had to keep it working. You know what breaks. You have built evaluation and monitoring for ML systems rather than adopting a vendor dashboard and hoping. You can reason about where a pipeline’s quality actually comes from and say so when a metric is measuring the wrong thing. Ownership You are comfortable with ambiguity and owning a domain end to end. This is a small team; there is no one to hand the pager to. You are willing to learn the mission domain. The engineers who do best here become genuinely interested in the logistics problem itself. Nice to Have Clearance: Preferred LLM serving and inference optimization (vLLM, TensorRT-LLM, quantization, prefix caching). Retrieval-grounded systems in production: hybrid retrieval, re-ranking, index freshness, and citation quality. Edge, on-premises, or disconnected deployment. Experience supporting ATO, continuous authorization, or production operations in classified environments. Defense, intelligence, or another accredited or regulated environment. Palantir Foundry, PostgreSQL/pgvector, NATS/JetStream, or ArgoCD. A degree in CS, engineering, or a related technical field, or the equivalent built the hard way. Mission and Identity We are building the system that enables faster, smarter logistics decisions in contested environments, and we're doing it with a team of seasoned entrepreneurs, operators, and technologists who have built and scaled solutions in this space before. We hold ourselves to an extremely high standard. We value clear thinking, direct communication, and the kind of ownership that doesn't stop until something actually works. Our mission is to create decision advantage when the stakes are the highest. If we succeed, the system doesn't just run; it gets smarter. We're not building AI for its own sake. We're building it because faster, smarter decisions in the most demanding environments on earth can't wait. If you want to work somewhere the stakes are real and the mission is urgent — you'll fit in here. Why Gallatin? The logistics infrastructure that supports America's warfighters and humanitarian disaster responders is overdue for transformation, and we are building it. From defense operations to disaster response, we're solving the hardest problems that keep missions moving when it matters most. Join a team where the mission is the point. Compensation: Gallatin offers competitive compensation commensurate with experience. Actual compensation may vary based on experience, skills, and location. In addition to base salary, we offer a generous equity grant, full healthcare coverage, 401k, unlimited PTO, and the perks of working in a high-caliber, mission-driven environment. Gallatin is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, sex, national origin, age, disability, veteran status, sexual orientation, gender identity, or any other characteristic protected by applicable federal, state, or local law. This position may require the ability to obtain and maintain a U.S. government security clearance. The successful candidate must be able to work in a classified environment when necessary. We comply with the United States Department of Labor's Pay Transparency provision . Note: Due to the nature of certain government contracts held by this organization, U.S. citizenship is a requirement for all positions at Gallatin. Proof of citizenship will be required prior to employment if selected. Compensation Range: $80K - $210K Show more Show less", "job_title": "Machine Learning Operations (MLOps) Engineer", "timestamp": "2026-10-05T04:47:53.602Z"}”