We are seeking an applied research scientist and research engineer with a background in computational mathematics and physics simulation to join our team within Meta's Reality Lab Research. As a member of our research group, you will play a crucial role in developing a large-scale software library for physics simulations that will drive innovation in robotics and embodied AI applications. You will leverage their expertise in software engineering, computational mathematics, and physics simulations to design, develop, and optimize simulation tools that will enable the development of controls for embodied agents in the physical world. You will work closely with a team of researchers and engineers to develop state-of-the-art simulators and is expected to operate across low-level high-performance software development as well as high-level architectural design.
Responsibilities
Lead complex software system integration and development Implement state-of-the-art algorithms for numerical linear algebra, PDEs, optimization/dynamic programming/RL Collaborate with cross-functional teams to identify and prioritize project requirements Develop and maintain high-quality, efficient, and scalable code Participate in code reviews and contribute to the development of best practices
Qualifications
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience Bachelor's degree in Computer Science, Computational Mathematics, Computational Mechanics, relevant technical field, or equivalent practical experience Ph.D. in Computer Science, Computational Mathematics, Computational Mechanics, or relevant degree and 3+ years experience in large scale computational mathematics/physics software 3+ years experience with developing high performance computing solutions in C++ 3+ years experience with parallel computing on CPU and GPUs 3+ years experience with physics simulations Experience with state-of-the-art simulation libraries Experience with controls theory and training embodied agents in physical interactions Experience with differentiable physics/PDE constrained optimization Experience with dimensionality reduction and reduced order modeling Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies