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Shield AI is seeking a Staff Engineer to design, implement, train, and evaluate reasoning and cognition models for unmanned autonomous platforms. This role sits at the intersection of foundation models, deep learning, and autonomous systems, with direct impact on defense and civilian protection applications.
You will own the full lifecycle of reasoning model development: designing and validating tradeoffs among foundation-model approaches (vision-language models, vision-language-action models, specialized deep-learning, and traditional AI/ML); developing capabilities for context interpretation, temporal reasoning, multi-agent coordination, and adaptation to incomplete information; and building reproducible training workflows combining self-supervised learning, supervised fine-tuning, imitation learning, reinforcement learning, and distillation.
Key responsibilities include defining data requirements, training objectives, benchmarks, and evaluation methods; establishing rigorous evaluations in simulation and with real-world data; incorporating edge constraints into model architecture and training in partnership with hardware and autonomy teams; diagnosing model failures and creating feedback loops; delivering trained models with reproducible results; and communicating technical tradeoffs and model limitations to research, engineering, product, and leadership.
Shield AI operates globally with offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific. The company develops Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation technologies actively supporting operations worldwide.
REQUIREMENTS:
- BS/MS in Computer Science, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, or equivalent practical experience. Typically 7+ years with Bachelor's; 6+ years with Master's; 4+ years with PhD; or equivalent work experience.
- Demonstrated track record of developing and deploying advanced ML models in production environments with substantial hands-on contributions to model design, training, and evaluation.
- Deep expertise in foundation models, modern deep learning, model architectures, and the training ecosystem.
- Strong command of training and adaptation approaches: self-supervised learning, supervised fine-tuning, imitation learning, reinforcement learning, and distillation.
- Experience defining datasets, training objectives, experimental workflows, benchmarks, and reproducible comparison criteria.
- Fluency in traditional AI/ML techniques and ability to combine them thoughtfully with learned models.
- Proficiency with training frameworks (PyTorch, JAX, TensorFlow, etc.).
- Experience with multimodal, embodied, robotic, or autonomous-system models; familiarity with VLA models and world models particularly relevant.
- Familiarity with traditional robotics algorithms (path planning, probabilistic modeling, optimization).
- Familiarity with NVIDIA Jetson and other edge compute platforms (Qualcomm, AMD, etc.).
PREFERRED:
- MS or PhD in Computer Science, Electrical Engineering, or similar.
- Expertise in optimizing and deploying ML models to resource-constrained hardware.
- Experience with simulation-to-real on autonomous systems.
- Experience with unmanned autonomous systems in defense applications.
- Familiarity with large, distributed systems.
- Familiarity with high-fidelity simulation (rigid body dynamics, sensor/actuator models).
