Principal Architect - Navigation (AI/ML) for Robotics Unicorn

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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.