AI/ML Engineer
Designs and implements LLM orchestration frameworks and agent reasoning systems for adaptive mission planning in multi-domain unmanned systems. Optimizes AI models for edge deployment on autonomous vehicles, integrating with ROS autonomy stack for mission-critical operations.
About the job
Key Responsibilities
- Design and implement LLM orchestration frameworks for mission planning and task decomposition across heterogeneous vehicle fleets
- Develop agent reasoning systems that bridge high-level mission objectives with executable autonomy commands
- Optimize large and quantize language models and agent frameworks for deployment on edge computing hardware (Jetson, companion computers)
- Manage the full lifecycle of AI agents including model versioning, prompt engineering, tool integration, and memory management
- Implement human-in-the-loop workflows that provide transparent, explainable AI reasoning to operators
- Integrate AI reasoning outputs with autonomy middleware (e.g., ROS 2) to enable seamless mission execution across heterogeneous
- Build evaluation, monitoring, and logging systems to track agent performance, reliability, and cost in operational environments
- Develop safe deployment and rollback practices for AI agents in mission-critical scenarios
- Collaborate with autonomy engineers to ensure AI-generated plans are executable and safe across multi-domain platforms
- Validate agent behaviors through simulation-in-loop testing before field deployment
- Design AI systems that maintain effectiveness in denied, degraded, and contested communication environments
Required Qualifications
- 3+ years of experience in production AI/ML applications with emphasis on LLM deployment and orchestration
- Proficiency in Python and modern AI/ML frameworks (PyTorch, Transformers, 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 practices, including 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 approaches
- U.S. Citizenship with the ability to obtain a security clearance
Preferred Qualifications
- Experience with multi-agent coordination frameworks and distributed AI reasoning systems
- Background in robotics or autonomous systems integration (ROS 2, navigation stacks, sensor fusion)
- Familiarity with reinforcement learning for planning and decision-making applications
- Understanding of secure coding practices and adversarial robustness in AI-driven systems
- Experience deploying AI models to embedded hardware (Jetson, Raspberry Pi, or similar edge devices)
- Exposure to simulation-in-loop and hardware-in-loop testing environments
- Knowledge of autonomous vehicle domains (UAVs, USVs, UUVs) and associated protocols
- Background in structured data preparation and feature engineering for AI ingestion
- Contributions to open-source AI or robotics projects
- Experience contributing to mission assurance and safety cases, including field-readiness reviews
- Experience collaborating with security and compliance teams on logging, auditability, and data-handling requirements for fielded AI systems
Skills
Python, PyTorch, Transformers, LangChain, Ros 2, MLOps, LLMs, Prompt Engineering, Model Optimization, Quantization, Jetson, Kubernetes
Similar jobs
ML Engineering jobs
Thinking Machines Lab
San Francisco, CA
Build and operate the engineering systems that support post-training research, including reinforcement learning infrastructure, sandboxed execution, data pipelines, and agent scaffolding. The role requires strong Python and systems engineering skills, project ownership, and a relevant bachelor’s degree or equivalent experience.
OpenAI
San Francisco, CA
Build research infrastructure and tooling that enables AI models to design silicon, including reinforcement learning environments, EDA integrations, evaluations, and experiment workflows. The role requires strong software engineering fundamentals and comfort working across research, tooling, and chip-design systems.
Rollstack
United StatesBuild production AI capabilities for automated slide and document generation, working across LLM applications, data analysis, and content generation. The role requires 3+ years in machine learning and NLP, advanced Python, and experience with LLM frameworks and production systems.
ClickUp
United States
Build and operate large-scale ranking and retrieval systems that power search relevance, including hybrid lexical/vector search, embeddings, query understanding, and permission-aware retrieval. Requires a bachelor's degree and 5+ years of ML engineering experience in ranking or information retrieval.
PathAI
Boston, MADevelop and deploy machine learning models for biomedical research and AI products, collaborating with scientific, engineering, and product teams. Requires an advanced quantitative degree, substantial ML experience, Python proficiency, and experience bringing models into production or research applications.