ML Systems Engineer, Robotics
Core
Design and build platforms for scalable, reliable, and efficient serving of foundation models tailored for physical agents in robotics and autonomous vehicles.
Role type
ML Systems Engineer (Robotics/Physical AI)
Builds
Internal platforms for model capability discovery and high-performance serving of foundation models for physical agents.
Domain
Robotics, Autonomous Vehicles, Computer Vision, Physical AI
Deliverable
production ML models | infrastructure
Required skills
backend system design, machine learning infrastructure, GPU-level algorithm optimization (CUDA, kernel tuning), systems-level programming (Python, Go, Rust, C++), serving and routing fundamentals, container orchestration (Kubernetes), infrastructure as code (Terraform)
Preferred skills
Vision-Language-Action (VLA) models, high-performance video processing (FFmpeg, NVDEC/NVENC), 3D data handling (point clouds), robotics middleware (ROS/ROS2)
Technologies
Python, Go, Rust, C++, CUDA, Docker, Kubernetes, AWS, GCP, Terraform, FFmpeg, NVDEC, NVENC, ROS, ROS2
Responsibilities
Maintain fault-tolerant, high-performance systems for serving robotics-related models at scale; Build an internal platform to empower model capability discovery; Collaborate with Robotics researchers and Computer Vision engineers to integrate and optimize models; Conduct architecture and design reviews to uphold best practices in system scalability, reliability, and security; Develop monitoring and observability solutions to ensure system health and real-time performance tracking; Own projects end-to-end from requirements gathering to implementation.
Seniority
Mid-Senior, hands-on IC