10 months ago
Austin, TX, USAMid Level
Responsibilities
- Design and implement LLM orchestration frameworks for mission planning and task decomposition across heterogeneous vehicle fleets.
- Develop agent reasoning systems that connect mission objectives to executable autonomy commands.
- Optimize, quantize, and deploy language models and agent frameworks on edge hardware such as Jetson and companion computers.
- Manage AI-agent lifecycles, including model versioning, prompt engineering, tool integration, and memory management.
- Implement explainable human-in-the-loop workflows for operators.
- Integrate AI reasoning outputs with ROS 2 autonomy middleware for mission execution.
- Build evaluation, monitoring, and logging systems for agent performance, reliability, and cost.
- Develop safe deployment and rollback practices for mission-critical AI agents.
- Collaborate with autonomy engineers to ensure AI-generated plans are executable and safe.
- Validate agent behavior through simulation-in-loop testing before field deployment.
- Design AI systems for denied, degraded, and contested communication environments.
Requirements
- At least 3 years of experience in production AI/ML applications, with emphasis on LLM deployment and orchestration.
- Proficiency in Python and modern AI/ML frameworks such as PyTorch, Transformers, and LangChain or equivalent orchestration tools.
- Experience with model optimization, quantization, and deployment to edge computing environments.
- Understanding of distributed systems and real-time AI inference requirements.
- Familiarity with MLOps, model versioning, monitoring, and lifecycle management.
- Knowledge of prompt engineering, agent framework design, and multi-step reasoning systems.
- Experience with constraint solving, planning algorithms, or symbolic reasoning.
- U.S. citizenship and ability to obtain a security clearance.
- Preferred experience includes multi-agent coordination, distributed AI reasoning, robotics, ROS 2, navigation stacks, sensor fusion, reinforcement learning, secure coding, adversarial robustness, and embedded hardware deployment.
- Preferred qualifications also include simulation-in-loop or hardware-in-loop testing, autonomous vehicle domains and protocols, structured data preparation, feature engineering, open-source AI or robotics contributions, mission assurance, safety cases, field-readiness reviews, and collaboration with security and compliance teams.
Benefits
- Hybrid on-site work in Austin, Texas, with up to 20% travel.
- Comprehensive medical, dental, and vision plans.
- 401(k) retirement savings plan with company match.
- Equity grants for new hires.
- Unlimited paid time off and 11 paid holidays.
- Generous company holiday calendar, including a November and December holiday hiatus.
- Parental leave and military leave.
- FSA, DCFSA, and HSA programs.
- Professional development opportunities.
- Access to One Medical.
- Free 24/7 mental health resources plus legal and financial work-life services.
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