Deft Robotics
Senior Machine Learning Engineer, Robot Learning (Full-time)
Deft Robotics, San Francisco, California, United States, 94199
Senior Machine Learning Engineer, Robot Learning (Full-time)
Direct message the job poster from Deft Robotics
Our mission is to build the world’s first labor agency that deploys dexterous robots as its primary workforce.
We start by deploying wheeled humanoid robots in industrial manufacturing and assembly lines.
You’ll be joining a team of extremely hardcore and self-motivated engineers, scientists, and operators who focus on winning 24/7.
As a founding member of the technical staff, you will develop and own entire systems from design to deployment, playing a foundational role in deploying 5000+ robots by 2031.
What you’ll do
Design and develop robot autonomy software stack and algorithms to enable capabilities including grasping and more dexterous behaviors in unstructured environments
Research and implement state-of-the-art robot learning policies, including reinforcement learning and imitation learning based techniques
Build reliable, high-speed robot autonomy software stack optimized for inference performance
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
Required Qualifications
Master's/PhD 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
Expected Compensation
$120,000 - $250,000 annual salary + cash and stock awards + benefits
Hiring Process
Phone screen + 3 virtual technical interviews + onsite
Seniority level Mid‑Senior level
Employment type Full‑time
Job function Engineering and Information Technology
Industries Manufacturing
#J-18808-Ljbffr
Our mission is to build the world’s first labor agency that deploys dexterous robots as its primary workforce.
We start by deploying wheeled humanoid robots in industrial manufacturing and assembly lines.
You’ll be joining a team of extremely hardcore and self-motivated engineers, scientists, and operators who focus on winning 24/7.
As a founding member of the technical staff, you will develop and own entire systems from design to deployment, playing a foundational role in deploying 5000+ robots by 2031.
What you’ll do
Design and develop robot autonomy software stack and algorithms to enable capabilities including grasping and more dexterous behaviors in unstructured environments
Research and implement state-of-the-art robot learning policies, including reinforcement learning and imitation learning based techniques
Build reliable, high-speed robot autonomy software stack optimized for inference performance
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
Required Qualifications
Master's/PhD 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
Expected Compensation
$120,000 - $250,000 annual salary + cash and stock awards + benefits
Hiring Process
Phone screen + 3 virtual technical interviews + onsite
Seniority level Mid‑Senior level
Employment type Full‑time
Job function Engineering and Information Technology
Industries Manufacturing
#J-18808-Ljbffr