RoboForce
Senior ML Research Engineer, Manipulation
RoboForce, Milpitas, California, United States, 95035
Senior Ml Research Engineer, Manipulation
We are looking for a Senior Research Engineer to advance robotic manipulation capabilities, enabling dynamic and physically interactive tasks. You will develop algorithms for grasping, motion planning, and tactile learning in real-world environments. Responsibilities: Design and deploy manipulation algorithms for high-DOF robotic tasks (e.g., grasping, connecting, picking, placing, etc). Develop motion planning models for dynamic environments. Deploy models as production-ready solutions on RoboForce robots. Create and enhance contact-rich robot learning stacks through physics-based simulation. Qualifications: Master's degree in Machine Learning, Robotics, or related field with 4+ years of experience or a PhD degree. Proficiency in Python, and deep learning frameworks (e.g., PyTorch, JAX). Decent understanding of multimodal models, modern ML architectures (transformers, diffusion models, etc.). Expertise in imitation learning, reinforcement learning, tactile sensing and robotics learning. Proficiency with one or more physical simulators (e.g., MuJoCo, IsaacSim, Drake, PyBullet, PhysX) and experience working in a deployed robotics environment. Requires 5 days/week in-office collaboration with the teams. Preferred Skills: Strong publication on top conferences in robotics manipulation. Expertise in neural network deployment (e.g., TensorRT) and GPU programming with CUDA. Proven ability to design scalable experimentation and data pipelines. Familiarity with 3D computer vision and/or graphics pipelines Experience with Large Language Model. Expertise in C++ programming.
We are looking for a Senior Research Engineer to advance robotic manipulation capabilities, enabling dynamic and physically interactive tasks. You will develop algorithms for grasping, motion planning, and tactile learning in real-world environments. Responsibilities: Design and deploy manipulation algorithms for high-DOF robotic tasks (e.g., grasping, connecting, picking, placing, etc). Develop motion planning models for dynamic environments. Deploy models as production-ready solutions on RoboForce robots. Create and enhance contact-rich robot learning stacks through physics-based simulation. Qualifications: Master's degree in Machine Learning, Robotics, or related field with 4+ years of experience or a PhD degree. Proficiency in Python, and deep learning frameworks (e.g., PyTorch, JAX). Decent understanding of multimodal models, modern ML architectures (transformers, diffusion models, etc.). Expertise in imitation learning, reinforcement learning, tactile sensing and robotics learning. Proficiency with one or more physical simulators (e.g., MuJoCo, IsaacSim, Drake, PyBullet, PhysX) and experience working in a deployed robotics environment. Requires 5 days/week in-office collaboration with the teams. Preferred Skills: Strong publication on top conferences in robotics manipulation. Expertise in neural network deployment (e.g., TensorRT) and GPU programming with CUDA. Proven ability to design scalable experimentation and data pipelines. Familiarity with 3D computer vision and/or graphics pipelines Experience with Large Language Model. Expertise in C++ programming.