Title: Principal Architect – Navigation (AI/ML) (RL & Simulation focused)
Location: San Francisco, CA (Hybrid - 3days onsite) (Relocation allowances will be given)
Job Type: Full-time
Compensation - 150k to 300k + Annual Bonus + Insurances (Health, Life, Dental, Vision)
Job Description:
About the Role
We are seeking a highly skilled and visionary Principal Architect – Navigation (AI/ML)for a robotics unicorn based in San Francisco, CA. Backed by over $500 million in funding from top-tier investors, our client is at the forefront of developing cutting-edge machine learning technology. They are building pioneering intelligent automation systems that power some of the world’s largest warehouses and retail operations.
This role needs design and development of cutting-edge navigation systems powered by machine learning and deep learning. This role is critical to driving innovation in intelligent path planning and autonomous decision-making, with real-world applications in robotics, logistics, warehouse automation, and beyond. You will play a key leadership role in shaping our ML-driven navigation stack—from research and prototyping to production deployment in high-scale environments.
Key Responsibilities
·Architect and build robust ML and deep learning models for navigation and control systems.
·Design and implement reinforcement learning agents within simulation environments.
·Drive end-to-end development and deployment of production-grade ML models.
·Collaborate closely with cross-functional teams across robotics, perception, and infrastructure.
·Evaluate and integrate classical and modern path planning algorithms (e.g., A*, RRT, etc.)
·Leverage simulation tools to test and validate navigation models in virtual environments.
·Guide the implementation of MLOps best practices, including data pipelines, training, deployment, and monitoring.
·Stay ahead of emerging trends in AI, reinforcement learning, and robotics.
Must-Have Technical Expertise
·Strong expertise in machine learning and deep learning frameworks (e.g., TensorFlow, PyTorch).
·Hands-on experience in building and deploying production-grade ML models.
·Demonstrated experience with simulation environments (e.g., Gazebo, CARLA, Unity).
·Deep understanding and practical application of reinforcement learning algorithms.
·Proficiency in classical and modern path planning algorithms (A*, RRT, D* Lite, etc.).
·Solid understanding of robotics fundamentals, such as kinematics and motion control.
·Experience working in physical domains like warehouse automation, autonomous vehicles, or logistics.
·Familiarity with cloud-based ML services (e.g., Google Vertex AI, AWS Sagemaker, Azure ML).
·Knowledge of the full ML lifecycle, including data collection, training, MLOps, and CI/CD deployment pipelines.
Nice-to-Have Qualifications
·Published research in AI, Robotics, or Navigation in reputed journals or conferences.
·Expertise in multi-robot path planning algorithms and coordination strategies.
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