Computer Vision AI & ML Engineer

About the Role

We are seeking a hands-on Computer Vision AI & ML Engineer with 3–10 years of experience to design, train, and deploy the perception systems behind our general-purpose robotic foundation model. In this role, your work will serve as the "eyes" of our AI brain. You will own the full ML lifecycle—from architecture design and data ingestion to model optimization and deployment on physical robots executing real-time multi-modal perception in the physical world.

What You Will Do

• Develop Core Perception Models: Design, train, and optimize deep learning models for depth estimation, 3D object detection, semantic/instance segmentation, tracking, and 3D scene understanding using multi-modal sensor streams (RGB-D, LiDAR, stereo vision).
• Build Production Pipelines: Construct scalable end-to-end pipelines for data processing, training, evaluation, and deployment into real-time physical robotic systems.
• Data & Annotation Engineering: Design automated tooling and strategies for synthetic/real data generation, labeling, QA, dataset management, versioning, and augmentation.
• System Reliability & Monitoring: Implement robust monitoring and failure-detection frameworks, including uncertainty estimation and automated performance diagnostics.
• Cross-Functional Collaboration: Partner directly with robotics, hardware, systems, and simulation teams (using Isaac Sim, Gazebo, Blender) to bring research proof-of-concepts into production environments.

Key Requirements

Minimum Qualifications

• Experience: 3–10 years of hands-on experience as an AI/ML Engineer or Computer Vision Engineer.
• Education: BS or MS in Computer Science, Robotics, Electrical Engineering, or a related field.
• Core CV Expertise: Strong background in 3D scene understanding, depth estimation, detection, segmentation, and tracking.
• Deep Learning Frameworks: Proficiency with PyTorch, TensorFlow, or JAX.
• Programming Languages: High proficiency in Python; working knowledge or familiarity with C++ for deployment pathways.
• ML Lifecycle & Infrastructure: Proven track record building end-to-end training pipelines, evaluation frameworks, deployment workflows, dataset management tools, and augmentation strategies.
• On-Site Requirement: Willing and able to work 5 days/week on-site at our San Mateo, CA headquarters (no relocation support offered).

Preferred Qualifications

• Experience working in high-growth robotics, autonomous vehicles, or IoT startup environments.
• Deep mathematical understanding of 3D geometry, sensor calibration, state estimation, and multi-sensor fusion (RGB-D, LiDAR, stereo).
• Familiarity with simulation and rendering tools such as Isaac Sim, Gazebo, or Blender.

Tech Stack

PyTorch, TensorFlow, JAX, Python, C++, RGB-D, LiDAR, Stereo Vision, Isaac Sim, Gazebo, Blender