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.
Skills Required
- PhD or MS degree in Computer Science, Machine Learning, Robotics, or an equivalent technical discipline
- Deep expertise in machine learning fundamentals and reinforcement learning
- Experience with machine learning frameworks such as PyTorch, TensorFlow, and Ray
- At least 3 years of experience developing and deploying machine learning systems from research through production
- Hands-on experience with machine learning model lifecycle management, including training, deployment, and production maintenance
- Peer-reviewed publications in robotics or related fields
- Experience designing and implementing bimanual manipulation technology stacks using imitation learning or reinforcement learning
- Background in real-time machine learning inference systems, sim-to-real transfer, or advanced reinforcement learning implementations
What We Do
Deft AI, Inc., operating as Deft Robotics, deploys wheeled humanoid robots and robot-foundation-model-based work cells in manufacturing facilities. Its systems integrate with factory workflows to automate difficult tasks that still require people, including final assembly, cable handling, line feeding, and kitting. The company focuses on automotive and electronics manufacturers, combining robotics, AI, and on-site integration to help factories improve throughput and address labor-intensive processes.
