4 days ago
Base Salary
$240k - $360k/yr
Responsibilities
- Design, implement, train, adapt, and evaluate reasoning and cognition models for unmanned autonomous platforms.
- Evaluate foundation-model, vision-language, vision-language-action, specialized deep-learning, and traditional AI/ML approaches.
- Develop capabilities for contextual interpretation, temporal reasoning, multi-agent coordination, and adaptation to incomplete or changing information.
- Build reproducible workflows using self-supervised learning, supervised fine-tuning, imitation learning, reinforcement learning, and distillation.
- Define datasets, training objectives, benchmarks, and evaluation methods for comparing model approaches.
- Evaluate model generalization, robustness, uncertainty, and performance across simulation, real-world data, team sizes, and operating conditions.
- Incorporate edge-compute constraints into model architecture and training decisions with hardware, autonomy, and inference teams.
- Diagnose model failures, establish feedback loops, and deliver trained models, reproducible results, and performance evidence.
- Communicate technical tradeoffs, experimental findings, and model limitations to research, engineering, product, and organizational leadership.
Requirements
- Bachelor’s or master’s degree in computer science, electrical engineering, mechanical engineering, aerospace engineering, or a similar field, or equivalent practical experience.
- Typically 7 years of related experience with a bachelor’s degree, 6 years with a master’s degree, 4 years with a PhD, or equivalent work experience.
- Demonstrated experience developing and deploying advanced machine-learning models in production, including hands-on model design, training, and evaluation.
- Deep expertise in foundation models, modern deep learning, model architectures, and the training ecosystem.
- Strong command of self-supervised learning, supervised fine-tuning, imitation learning, reinforcement learning, and distillation.
- Experience defining datasets, training objectives, experimental workflows, benchmarks, and reproducible model-comparison criteria.
- Fluency in traditional AI/ML techniques and experience combining them with learned models.
- Experience with training frameworks such as PyTorch, JAX, or TensorFlow.
- Experience with multimodal, embodied, robotic, or autonomous-system models; familiarity with vision-language-action models and world models is particularly relevant.
- Familiarity with robotics algorithms including path planning, probabilistic modeling, and optimization.
- Familiarity with NVIDIA Jetson and other edge-compute platforms such as Qualcomm and AMD.
- Preferred qualifications include an MS or PhD, resource-constrained ML deployment expertise, simulation-to-real experience, defense autonomy experience, distributed-systems familiarity, and high-fidelity simulation knowledge.
Benefits
- Full-time regular employees receive bonus, benefits, and equity in addition to pay within the applicable range.
- Temporary employees receive a temporary benefits package after 60 days of employment.
- Offers are contingent on a cleared background and possible reference check.
- Military fellows and part-time employees are not eligible for benefits.
Tech Stack
PyTorchTensorFlow
Categories
ML EngineeringRobotics
About Shield AI
Shield AI builds autonomous systems for military and national security customers, combining its Hivemind autonomy software with V-BAT and X-BAT unmanned aircraft and Aechelon simulation technologies. The privately held company sells hardware, software, and related services to U.S. and allied defense agencies. Founded in 2015 and headquartered in San Diego, it operates across the U.S., Europe, the Middle East, and Asia-Pacific, and its technology is used in operational deployments.