Position Overview
In this role, you will drive the development of high-performing and resource-efficient reinforcement learning (RL) methods, tailored for the unique challenges of robotics, physical AI, and scheduling / OR systems. As a core member of the Digital Twins team, you will collaborate with researchers and ML engineers to design RL-driven solutions for complex industrial environments, and integrate them seamlessly with high-fidelity simulations and real-world data pipelines.
Responsibilities
- Design, implement, and optimize RL, imitation learning for a variety of systems including robotics, scheduling and routing.
- Build scalable, transferable, and production-ready codebases using PyTorch.
- Explore and prototype novel learning approaches that push the boundaries of efficiency and adaptability.
- Mentor and lead a team for publications, patents, and open research collaborations.
- Independently identify opportunities, propose solutions, and execute with minimal oversight while aligning with evolving company needs.
Must-Have Qualifications
- Ph.D. or M.S. in Computer Science, Artificial Intelligence, Robotics, Engineering, Operations Research or a closely related field, with strong emphasis on RL.
- Min. (PhD + 2) / (MTech + 5) years of research experience in RL with a strong publication record in top venues
- Strong programming skills in Python, with proven expertise in PyTorch or equivalent, and a track record of writing clean, modular, and deployable code.
- Solid foundation in machine learning with specialization in RL, imitation learning.
- Familiarity with advanced ML architectures such as graphs and transformers.
- Self-starter mindset: industrious, independent, and able to generate ideas and drive them forward without waiting for direction.
- Excellent problem-solving skills and adaptability to shifting research directions.
- Strong collaboration skills, with experience working in interdisciplinary and fast-paced teams.
Nice-to-Have Qualifications
- Familiarity with LLMs, agentic AI and vision models is a plus.
- Hands-on experience applying RL, imitation learning either in industrial / robotics settings or in scheduling / OR settings.
