about 4 hours ago
Responsibilities
- Implement and deploy state-of-the-art RL algorithms for locomotion and manipulation tasks.
- Drive the development cycle from simulation prototyping to policy fine-tuning on robots.
- Optimize the RL training pipeline for faster iterations and high-throughput simulation.
- Mentor junior engineers through technical guidance and code reviews.
- Collaborate with robotics and hardware teams to solve system-level issues.
- Analyze hardware results to inform technical directions and company objectives.
Requirements
- 5+ years of hands-on expertise with RL frameworks like PyTorch and high-fidelity simulators.
- Mastery of Python for rapid prototyping and strong proficiency in C++.
- Experience with large-scale distributed training pipelines and their optimization.
- Strong theoretical understanding of modern reinforcement learning techniques.
- Intuition for robot dynamics and controls theory applicable to learning-based approaches.
- A results-oriented mindset with a passion for real-world algorithm implementation.
- PhD or MS in Computer Science, Robotics, or a related field with 2+ years of industry experience preferred.
- Proven track record of deploying learning-based policies on physical robotic systems.
- Experience mentoring engineers in a team environment.
- Strong publication record in relevant conferences or journals is a plus.