Research Scientist

Deft Robotics; San Francisco (CA); On-site; USD 150,000 - 250,000; Full time

Benefits offered by this job

Medical, dental & vision plans

Daily meals stipend

Conference travel support

Job summary

Deft Robotics is seeking a skilled researcher/engineer to design and implement robot autonomy software stacks for grasping and dexterous manipulation in unstructured environments. You will explore state-of-the-art learning policies, including reinforcement and imitation learning, and deploy optimized, high-speed software for production use.

The role requires deep ML expertise, 3+ years of experience deploying ML systems, and strong experience with model lifecycle management.

Qualifications

• PhD or MS 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.

Responsibilities

• Design and develop robot autonomy software stack and algorithms for grasping and dexterous behaviors.
• Research and implement state-of-the-art robot learning policies, including RL and imitation learning.
• Build high-speed autonomy software optimized for inference performance.
• Design and maintain data collection pipelines for production robot fleets.
• Optimize policies for distributed training and real-time edge deployment.
• Ship production quality, safety-critical software.

Skills

Machine learning fundamentals

Reinforcement learning

ML systems deployment

Education

PhD or MS in Computer Science, ML, Robotics

Tools

PyTorch

TensorFlow

Ray

Job description

What you'll do

• Design and develop robot autonomy software stack and algorithms to enable capabilitiesincluding grasping and more dexterous behaviors in unstructured environments
• Research and implement state-of-the-art robot learning policies, including reinforcementlearning and imitation learning-based techniques
• Build reliable, high-speed robot autonomy software stack optimized for inferenceperformance
• Design and maintain robust data collection and curation pipelines for production robot fleets
• Optimize robot policies for distributed training at scale and real-time edge deployment
• Ship production quality, safety-critical software
• Advance SOTA dexterous manipulation research 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 associatedframeworks (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 stackswith imitation learning or RL-based methods
• Background in real-time ML inference systems, simulation-to-reality transfer, or advancedreinforcement 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 - $250,000 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.

Location and Work Mode

This is a full-time, five-day-a-week onsite role in our San Francisco, CA office.

US Work Authorization

US work authorization required: US Citizen, Green Card Holder, or Valid Visa Holder (H1-B, O-1, TN, E-3, OPT, etc).