Staff Engineer, Reinforcement Learning (R3639)
Shield AI
What You'll Do:
- Design, implement, and deploy reinforcement learning algorithms for a variety of platforms
- Collaborate with teams across the organization to integrate RL solutions that meet customer specifications
- Analyze and optimize performance of deployed RL models in dynamic environments
- Develop tools and infrastructure to support large-scale training, simulation, and evaluation
- Mentor and provide technical guidance to junior engineers
- Stay current with the latest advancements in RL, and apply them to solve challenging problems
- Contribute to the design and architecture of scalable, maintainable software systems
Required Qualifications:
- Master's degree in Computer Science, Robotics, or a related field and 5+ years of relevant professional experience or PhD with 4+ years of relevant experience.
- Familiarity with prototyping in Python is welcome, but this role demands professional C++ production deployment skills. Candidates whose primary experience is in Python are unlikely to find this position a good fit.
- Demonstrated experience deploying reinforcement learning algorithms in production environments
- Strong background deploying RL algorithms in production following full Software development lifecycle
- Ability to independently deploy high-reliability code suitable for real-world autonomous systems
- Experience with RL frameworks (e.g., TensorFlow (C++), libtorch, etc.) and RL training environments (e.g., OpenAI Gymnasium, Google DeepMind Control Suite, etc.)
- Solid understanding of software engineering best practices, including version control, testing, and CI/CD
- Familiarity with CUDA
- Excellent problem-solving and communication skills
Preferred Qualifications:
- Peer reviewed publications related to RL
- Strong background in Robotics or autonomous systems
- Experience with multi-agent RL or distributed RL systems
- Familiarity with simulation environments (e.g. Isaac Sim, MuJoCo)
- Experience with cloud-based training and deployment
- Experience working in aviation, or other safety-critical domains