Robotics Software Engineer

You will own our learned manipulation capability end to end, focusing on grasping and contact-rich dexterous manipulation. You will take tasks from concept to policies running on real hardware. That covers collecting demonstration data, fine-tuning foundation models, deploying to the fleet, and validating performance in real-world conditions. You will need a strong foundation in robotics, manipulation, and control, solid programming ability, and a rigorous experimental mindset.

What You Will Do

• Build the teleoperation and recording pipeline that turns real deployments into training data
• Fine-tune open-weight vision-language-action (VLA) base policies on our data using LoRA or equivalent methods, and build the evaluation harness to measure them
• Develop grasping and contact-rich manipulation policies and controllers, and validate them on real hardware
• Export policies to a portable runtime (ONNX or equivalent) and deliver them to the fleet deployment pipeline as versioned artifacts that can be rolled back
• Re-run base model selection on a regular schedule so we can adopt stronger foundation models as the field moves
• Document code, algorithms, experiments, and results clearly

What We Are Looking For

• Degree in Robotics, Computer Science, Software Engineering, or a related field, or equivalent practical experience
• Hands-on experience with imitation learning from demonstrations
• Working knowledge of at least one current open VLA model family (e.g. OpenVLA, Octo, π0, GR00T)
• Experience exporting and deploying models through ONNX or an equivalent runtime
• Experience with GPU-accelerated simulation such as NVIDIA Isaac Sim or Isaac Lab
• Strong Python skills and a solid grounding in manipulation and control

Nice to Have

• Has shipped a learned policy onto real robot hardware
• Experience with multi-fingered end effectors or force/tactile sensing
• ROS 2
• Experience with manipulation benchmarks such as LIBERO
• Simulation experience with Gazebo or MuJoCo
• Cloud platforms (AWS, Azure, or GCP), Docker, Kubernetes, CI/CD, or MLOps practices
• Familiarity with LLM application architectures, orchestration, or agentic frameworks
• Comfortable working with AI-assisted coding tools