Machine Learning Engineer

What you'll do

• Design and develop a modular robot autonomy stack that composes Vision-Language-Action (VLA) models with purpose-built modules to enable grasping and dexterous behaviors in unstructured environments
• Develop action refinement and safety layers that post-process VLA outputs — constraint satisfaction, collision and force guards, smoothing, and runtime monitors for safety-critical deployment
• Architect clean interfaces and abstractions around base VLA models so they can be swapped, benchmarked, and upgraded as the SOTA evolves — keeping the stack model-agnostic
• Design and maintain robust data collection and curation pipelines for production robot fleets
• Build reliable, high-speed robot autonomy software stack optimized for inference

performance

• Advance SOTA dexterous manipulation architecture through novel methodologies while bridging theory & practice—real customer use-cases with clear success criteria.

Required Qualifications

• PhD or MS degree in Computer Science, Machine Learning, Robotics, or equivalent technical discipline
• Deep expertise in machine learning fundamentals, reinforcement learning, and associated

frameworks (PyTorch, TensorFlow, Ray, etc.)

• 3+ years of proven track record developing and deploying ML systems from research through production implementation
• Hands-on experience with model lifecycle management including training, deployment, and maintenance in production settings

Preferred Qualifications

• Authored or co-authored peer-reviewed publications in robotics or related fields
• Hands-on experience designing and implementing bimanual manipulation tech stacks

with imitation learning or RL-based methods

• Background in real-time ML inference systems, simulation-to-reality transfer, or advanced

reinforcement learning implementations

Benefits

• We support publishing at top robotics/ML venues and presenting at conferences (travel + time fully covered).
• Medical, dental & vision plans
• Daily meals stipend

Hiring Process

• Phone screen + 3 virtual technical interviews + onsite

Expected Compensation

• $150,000 - $200,000 annual salary + cash and stock awards + benefits
• The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.

Compensation Range: $150K - $200K