Robotics Reinforcement Learning Engineer

工作說明 / Job description: • 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).

資格需求 / Requirements: • 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.

薪資待遇 / Salary: 面議(將依學經歷進行敘薪)