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At Reality Labs Research (RL-R), our goal is to explore, innovate and design novel interfaces and hardware subsystems for the next generation of virtual, augmented, and mixed reality experiences. 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, the candidate 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 your 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'll work closely with a team of researchers and engineers to develop state-of-the-art simulators and are expected to operate across low-level high-performance software development as well as high-level architectural design.
Job Responsibility:
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
Requirements:
Bachelor's degree in Computer Science, Computer Engineering, 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
Nice to have:
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