Principal Architect – Navigation

4 hours ago

San Francisco, California, United States FlairX Full time

About the job

Job Title: Principal Architect – Navigation (AI/ML) (RL & Simulation focused)

Location: San Francisco, CA (Hybrid)

Job Type: Full-time

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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