Job Specifications
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