Shield AI builds autonomy systems for defense applications, including air, maritime, and space platforms operating in complex and contested environments.
We are establishing a centralized AI and Data Platform organization responsible for the infrastructure that underpins autonomy development across Hivemind and other programs. This team owns the systems used to train models, run simulation, manage data, and deploy models to operational environments.
We are seeking a Principal Engineer that will scale an initial architecture into a platform that supports multiple autonomy programs.
Success in this role requires disciplined execution, delivering fast iteration for engineering teams while maintaining reliability, cost control, and architectural consistency as the system scales.
The Principal Engineer is accountable for ensuring engineers can move efficiently from idea to trained model to deployed capability, and that infrastructure decisions reflect the realities of the domain, including simulation-driven development, continuously evolving multi-modal sensor data, and deployment to constrained and reliability-critical systems.
This role spans the full lifecycle of autonomy development, training foundation models, running large-scale and multi-fidelity simulation, managing training data, evaluating models, and deploying optimized models to edge systems.
A key part of this role is defining how these capabilities extend beyond internal use. This includes establishing how Shield AI delivers AI infrastructure in customer environments across on-premise, cloud, hybrid, and sovereign or nationally constrained environments.
What you'll do:
- Platform Ownership: Define and operate the core AI and data platform across training, simulation, data management, evaluation, and deployment.
- Compute Strategy and Infrastructure: Own where and how workloads run across on-premise, cloud, and hybrid environments. Drive capacity planning, utilization, and cost-per-compute decisions, including support for classified and air-gapped systems
- Training and Simulation Systems: Build infrastructure for distributed training (supervised learning, RL/MARL, foundation models) and large-scale, multi-fidelity simulation. Ensure training and simulation systems operate together without bottlenecks.
- Data Platform: Ingest and manage multi-modal sensor data (EO, IR, radar, EW, IMU). Establish dataset versioning, data lineage, feature storage, data cataloging, and classification-aware storage and access controls.
- MLOps, Evaluation, and Model Lifecycle: Establish a consistent workflow for experiment tracking, model registry, artifact provenance, and automated validation. Implement evaluation and V&V gates so models meet defined standards before deployment.
- Deployment and Operational Feedback: Own the pipeline from training to deployment, including model optimization (e.g., distillation, quantization, pruning), deployment to edge systems, monitoring, drift detection, and retraining triggers.
- Customer AI Infrastructure: Define how AI infrastructure is deployed in customer environments across on-premise, cloud, hybrid, and sovereign settings. Establish a consistent approach that avoids one-off solutions while adapting to operational constraints.
- Platform Standardization: Define common tools, interfaces, and workflows across teams. Reduce duplication while maintaining flexibility where needed.
- Cross-Team Partnership: Work directly with Hivemind and other autonomy teams to ensure the platform supports real workloads and evolves with program needs.
Key Outcomes:
- Faster iteration from idea to trained model to evaluated result
- High utilization of compute resources with clear visibility into usage and cost
- Simulation capacity that supports large-scale training without bottlenecks
- Consistent end-to-end lifecycle: development, evaluation, deployment, monitoring, and retraining
- Repeatable data loop: telemetry, scenario extraction, retraining, and redeployment
- Reliable deployment of optimized models to edge systems
- Broad platform adoption across autonomy programs
- Repeatable approach for deploying AI infrastructure in customer environments
- Training iteration cycles measured in days, not weeks
- Sustained high utilization of GPU resources under production workloads
Representative performance targets:
Required qualifications:
- Experience building and operating ML infrastructure at scale (100+ GPU clusters, distributed systems)
- Experience defining compute strategy, including on-premise vs cloud tradeoffs, capacity planning, and cost management
- Strong understanding of ML workloads, including foundation models, RL/MARL, simulation-based training, and fine-tuning
- Experience building data platforms with dataset versioning, lineage, and cataloging
- Ability to debug and resolve system issues when needed
Preferred qualifications:
- Experience in defense or classified environments (e.g., air-gapped systems, SCIFs)
- Experience with simulation-heavy ML systems (robotics, autonomy, or similar domains)
- Experience deploying and optimizing models for edge hardware
- Familiarity with HPC systems (schedulers, parallel storage, high-speed networking)
Why Join Us
You will define the infrastructure that supports the development and deployment of autonomy systems across Shield AI.
This role establishes the foundation for how models are trained, evaluated, and deployed, and directly impacts how quickly new capabilities are delivered into operational environments.
You will have ownership over systems and decisions that are often distributed across multiple teams at other organizations, with the opportunity to shape how AI infrastructure is built and used both internally and in customer environments.
Skills Required
- Experience building and operating ML infrastructure at scale (100+ GPU clusters, distributed systems)
- Experience defining compute strategy including on-premise vs cloud tradeoffs, capacity planning, and cost management
- Strong understanding of ML workloads including foundation models, RL/MARL, simulation-based training, and fine-tuning
- Experience building data platforms with dataset versioning, lineage, and cataloging
- Ability to debug and resolve system issues when needed
- Experience in defense or classified environments (e.g., air-gapped systems, SCIFs)
- Experience with simulation-heavy ML systems (robotics, autonomy, or similar domains)
- Experience deploying and optimizing models for edge hardware (distillation, quantization, pruning)
- Familiarity with HPC systems (schedulers, parallel storage, high-speed networking)
What the Team is Saying
Shield AI Compensation & Benefits Highlights
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Healthcare Strength — Company materials describe excellent medical, dental, and vision coverage alongside a mental‑health EAP. Site perks such as an onsite gym in DC and a gym discount in San Diego support a health‑focused offering.
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Retirement Support — Careers materials highlight a 401(k) with company match as part of the standard package. A Total Rewards overview emphasizes retirement features within a broader, transparent compensation view.
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Career-Linked Recognition & Rewards — Compensation for in‑demand technical and senior go‑to‑market roles is described as competitive, with visible engineering ranges and top‑end packages. This points to meaningful upside tied to role, level, and scarce skills.
Shield AI Insights
What We Do
At Shield AI, you won't wait years to see your work reach the field. You'll build hardware and software that operates in the real world right now, in the hands of the people who depend on it. Hivemind, our AI pilot, has been flying since 2018. It has flown more than 30 platforms, including an F-16, and it now sits under a U.S. Air Force production contract for Collaborative Combat Aircraft. When you write code or shape a system here, you contribute to technology with a proven flight record and a clear production future. V-BAT flies intelligence, surveillance, and reconnaissance missions with an operational record that stretches from Ukraine to the Indo-Pacific. It delivers eyes where they matter most, in the most demanding conditions on earth. The teams behind it watch their work get tested where the stakes are real. X-BAT takes its first flight this year. It's an AI-piloted fighter that needs no runway, built to operate where traditional aircraft can't. Join now and you help shape a program at its earliest, most formative stage. That's the kind of ground-floor work that defines a career. Do the most impactful work of your life, on problems that matter. Autonomy at this level asks a lot of you. You'll take on problems in perception, planning, and control that few teams anywhere are equipped to solve. You'll work across disciplines, from aerospace and robotics to machine learning and systems engineering, alongside people who hold themselves to an exacting standard and expect the same from you. Our mission is clear: protect service members and civilians with intelligent systems. That purpose runs through every decision, every design review, and every deployment. It's why the work here carries a weight you can feel. Ready to join our mission? Explore our open roles and find where you fit.
Why Work With Us
Founded in 2015 by a former Navy SEAL, Shield AI builds AI pilots and uncrewed aircraft. Veterans aren't an afterthought here, they're at every level. It's why the work carries weight: AI pilots and uncrewed aircraft flying real missions, from Ukraine to the Indo-Pacific, protecting service members and civilians.
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Hybrid Workspace
Employees engage in a combination of remote and on-site work.
