Software Engineer, Systems ML - Frameworks / Compilers / Kernels
Bellevue, WA | Menlo Park, CA | New York, NY | Remote, US
You will be working on one of the core areas such as PyTorch framework components, AI compiler and runtime, high-performance kernels and tooling to accelerate machine learning workloads on the current & next generation of MTIA AI hardware platforms. You will work closely with AI researchers to analyze deep learning models and lower them efficiently on MTIA hardware. You will also partner with hardware design teams to develop compiler optimizations for high performance. You will apply software development best practices to design features, optimization, and performance tuning techniques. You will gain valuable experience in developing machine learning compiler frameworks and will help in driving next generation hardware software codesign for AI domain specific problems.Software Engineer, Systems ML - Frameworks / Compilers / Kernels Responsibilities
- Development of SW stack with one of the following core focus areas: AI frameworks, compiler stack, high performance kernel development and acceleration onto next generation of hardware architectures.
- Contribute to the development of the industry-leading PyTorch AI framework core compilers to support new state of the art inference and training AI hardware accelerators and optimize their performance.
- Analyze deep learning networks, develop & implement compiler optimization algorithms.
- Collaborating with AI research scientists to accelerate the next generation of deep learning models such as Recommendation systems, Generative AI, Computer vision, NLP etc.
- Performance tuning and optimizations of deep learning framework & software components.
- Proven C/C++ programming skills
- Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
- Experience in AI framework development or accelerating deep learning models on hardware architectures.
- A Bachelor's degree in Computer Science, Computer Engineering, relevant technical field and 12+ years of experience in AI framework development or accelerating deep learning models on hardware architectures OR a Master's degree in Computer Science, Computer Engineering, relevant technical field and 8+ years of experience in AI framework development or accelerating deep learning models on hardware architectures OR a PhD in Computer Science Computer Engineering, or relevant technical field and 7+ years of experience in AI framework development or accelerating deep learning models on hardware architectures.
- Knowledge of GPU, CPU, or AI hardware accelerator architectures.
- Experience working with frameworks like PyTorch, Caffe2, TensorFlow, ONNX, TensorRT
- OR AI high performance kernels: Experience with CUDA programming, OpenMP / OpenCL programming or AI hardware accelerator kernel programming. Experience in accelerating libraries on AI hardware, similar to cuBLAS, cuDNN, CUTLASS, HIP, ROCm etc.
- OR AI Compiler: Experience with compiler optimizations such as loop optimizations, vectorization, parallelization, hardware specific optimizations such as SIMD. Experience with MLIR, LLVM, IREE, XLA, TVM, Halide is a plus.
- OR AI frameworks: Experience in developing training and inference framework components. Experience in system performance optimizations such as runtime analysis of latency, memory bandwidth, I/O access, compute utilization analysis and associated tooling development.
$85.10/hour to $251,000/year + bonus + equity + benefits
Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about benefits at Meta.
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Job Profile
Accommodations Benefits Bonus Equity Individual compensation Reasonable accommodations
Tasks- Analyze deep learning models
- Build
- Collaborate with AI researchers
- Design
- Develop
- Develop software stack
- Optimize AI frameworks
- Performance tuning
- Research
Access AI AI frameworks AI hardware architectures AI Training Algorithms Best Practices C C++ Caffe2 Color Compiler development Compilers Computer Engineering Computer Science Computer Vision CUDA Deep Learning Design Developing Engineering Frameworks Generative AI GPU Identity Inference Kernel Development Kernel programming Library acceleration Machine Learning ML NLP ONNX OpenCL OpenMP Optimization Organization Performance Tuning Physics Procedures Programming Python PyTorch Recommendation systems Research Software Development Strategy Systems Technical Techniques TensorFlow TensorRT Virtual reality
Experience7 years
EducationBachelor Bachelor's Bachelor's degree Bachelor's degree in Computer Science Computer Engineering Computer Science Design Engineering Equivalent Equivalent practical experience Master Master's Master's degree Ph.D. Physics Relevant technical field Technical field Technology
TimezonesAmerica/Anchorage America/Chicago America/Denver America/Los_Angeles America/New_York Pacific/Honolulu UTC-10 UTC-4 UTC-5 UTC-6 UTC-7 UTC-8 UTC-9