Principal Engineer – AI

Principal Engineer – AI / Machine Learning Location: Hybrid (Redwood City, CA) Employment Type: Full-Time Compensation: Open / Competitive + Full Benefits Interview Mode: In-Person (Redwood City, CA) About the Role We are seeking a Principal Engineer – AI/ML to lead the design and delivery of high-performance, real-time machine learning systems powering multi-agent path planning and intelligent robotic decision-making. This is a hands-on principal engineer role, focused on architecture, algorithms, and production-grade ML systems at scale. You will work closely with robotics, platform, and product teams to translate complex real-world problems into reliable, low-latency AI solutions. Key Responsibilities Architect, build, and optimize distributed systems for real-time path planning in multi-agent environments using Machine Learning and Reinforcement Learning Own the end-to-end ML lifecycle, including data ingestion, feature engineering, model training, validation, deployment, and monitoring Lead production deployment and lifecycle management of ML/RL models using MLOps platforms such as Vertex AI or equivalent Integrate ML pipelines with robotic orchestration and control systems to enable continuous learning and adaptive behavior Serve as a technical mentor and reviewer for ML engineers; define and uphold best practices for code quality, model performance, and system reliability Collaborate cross-functionally with software, robotics, product, and operations teams to deliver scalable ML solutions for real-world fulfillment challenges Debug, profile, and optimize ML systems for latency, throughput, and reliability in real-time and distributed environments Required QualificationsEducation B.E. or M.S. in Computer Science, Artificial Intelligence, Machine Learning, Robotics, or a related field Experience 8+ years of total professional experience, with 6+ years in AI / Machine Learning systems Proven success as a Senior or Principal Individual Contributor delivering production-grade ML systems Experience influencing architecture and technical direction without direct people-management responsibility Technical Expertise Strong expertise in path planning, graph search algorithms, optimization techniques, and multi-agent systems Deep experience of Machine Learning, Deep Learning, and Reinforcement Learning Hands-on experience with TensorFlow or PyTorch Demonstrated success in building, deploying, and maintaining ML models at production scale Strong MLOps experience, including: Model CI/CD pipelines Workflow and pipeline orchestration Model monitoring, drift detection, and retraining strategies Advanced proficiency in Python Solid understanding of distributed systems, concurrency, parallelism, and real-time processing Strong computer science fundamentals: algorithms, operating systems, networking, memory management, and performance tuning Experience with microservices, Docker, Kubernetes, and containerized workloads Experience with event-driven architectures and asynchronous processing Hands-on experience with cloud platforms, preferably Google Cloud Platform (GCP) and Vertex AI Nice to Have Familiarity with Erlang, Elixir, or other concurrency-first functional languages Seniority level Mid-Senior level Employment type Full-time Job function Engineering and Information Technology Industries Software Development