Robotics Reinforcement Learning Engineer

工作內容

• Build and maintain robotics simulation environments (e.g., Isaac Lab / Isaac Sim), including tasks, sensors, and domain randomization.
• Design, train, and iterate RL/IL control policies for locomotion/manipulation tasks.
• Develop reproducible training pipelines with experiment tracking, logging, and metrics.
• Define evaluation metrics and debug failure cases (reward/curriculum tuning, observation/action refinement).
• Collaborate cross-functionally to move toward sim-to-real deployment under safety/latency/compute constraints.
• Integrate perception/multimodal inputs into the control pipeline when needed.
• Produce engineering-quality deliverables (codebase, scripts, documentation, benchmarks, demos).

條件要求

• Education: BS or MS in Robotics, Computer Science, Machine Learning, or related field.
• Experience: 1-3 years industry experience or a recent graduate with a strong, hands-on academic portfolio (e.g., thesis/capstone/robotics lab work).
• Portfolio/Proof of execution: Strong portfolio (GitHub and/or video links) demonstrating RL applied to physical robots or complex simulated environments.
• Technical stack: Strong Python plus deep learning frameworks (PyTorch or JAX); working knowledge of C++.
• English: Proficient in English (listening, speaking, reading, writing); TOEIC 650+ preferred.
• Bonus (Nice to have): Hands-on legged locomotion (quadruped/biped) or robot arm manipulation; experience integrating computer vision or vision-language models into control policies; familiarity with QDD actuators.

薪資

面議(將依學經歷進行敘薪)