Reality Labs (RL) is responsible for delivering Meta’s vision of the next generation of wearable XR systems to enable the next great wave of human-oriented computing. The compute performance and power efficiency requirements of these future XR systems will require the best-in-class custom silicon solutions running the most efficient AI models to enable powerful new AI applications in an all-day wearable form factor.
Requirements
- Experience with Python and building complex silicon, design automation, or software systems
- Understanding of agentic workflows and AI tools
- Understanding of computer architecture, hardware design, and power and performance optimization fundamentals
- Experience with design, model deployment, and performance optimization of PyTorch models for AI accelerator architectures (e.g. systolic arrays, vector extensions, custom ASICs, etc)
- Experience applying and integrating AI to accelerate and improve hardware EDA/CAD methodology tool flows
- Experience in LLM architectures, using LLMs to automate tasks and flows, building agentic workflows, and/or fine-tuning AI models for hardware
- Experience or familiarity with Tensorflow, Pytorch, MLIR, XLA, JAX, or tensor-rt, and classic ML and CV algorithms like BERT, RNN, CNN, or similar
Responsibilities
- Collaborate with computer architects, software, ML and silicon researchers and engineers, to map and optimize ML workloads on various backend targets including CPU’s, DSP’s, and Deep Learning Accelerators
- Perform ML algorithm, software, hardware co-design to achieve best energy and performance efficiency
- Use AI to build new solutions that aid silicon design with the goal of increasing efficiency and quality of our systems
- Develop high performance C/C++ kernels and optimize domain specific compilers to port industry standard ML libraries to custom hardware
- Review SOTA research trends in hardware specific ML model optimizations and mapping
- Evaluate and integrate promising techniques into shipping products
- Run analysis/profiling, identify performance and power bottlenecks on the actual hardware, virtual platforms, simulators or emulators and provide feedback for optimizations across the stack
Other
- Currently has, or is in the process of obtaining, a PhD degree in Computer Science, Computer Engineering, Electrical Engineering, or relevant technical field
- Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment
- Intent to return to degree program after the completion of the internship/co-op
- Experience working and communicating cross functionally in a team environment
- Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as ISSCC, VLSI, DATE, DAC, ICCAD, ISCA, ASPLOS, MICRO, PLDI, NeurIPs, ICLR, HPCA or similar
