Senior Research Software Engineer

The Senior Research Software Engineer will provide shared, cross-project engineering support to help multiple teams accelerate discovery by building and optimizing machine learning infrastructure, improving performance on modern hardware, and enabling scalable execution in AWS and HPC environments.

What You Will Do

Design, build, and maintain ML/AI systems and research software in Python and C/C++. Develop and optimize machine learning training and inference pipelines for accelerator-based systems. Contribute to or integrate with compiler and IR frameworks such as MLIR, LLVM, XLA, IREE, TVM, or Halide.

Why It Might Be a Fit

This is a hands-on role with strong collaboration expectations, requiring strong communication skills and a collaborative working style. The ideal candidate will have experience working in research or research-adjacent environments, with an understanding of the scientific software lifecycle.

Requirements

• Minimum of seven years’ post-secondary education or relevant work experience
• BS or MS (or equivalent practical experience) in Computer Science, Computer Engineering, Data Science, or a closely related field
• Strong programming skills in Python/C/C++
• Experience working with ML frameworks such as PyTorch, TensorFlow, JAX, XLA, Triton, ONNX, Caffe2, or TensorRT
• Proven experience in deep learning at scale, familiarity with the “alphabet soup” of distributed computing (DP, TP, SP, CP, EP)
• Experience with production environments, including Git-based workflows
• Experience working in AWS cloud or HPC environments used for large-scale computation
• Prior experience in a research or research-adjacent environment, with an understanding of the scientific software lifecycle
• Strong communication skills and a collaborative working style
• Contributed to compiler infrastructures and optimization frameworks (MLIR, LLVM, XLA, TVM, IREE, Halide)
• Experience developing or optimizing high-performance with libraries or kernels (e.g., cuBLAS, cuDNN, CUTLASS, HIP, ROCm, or similar)
• Experience with distributed AI/ML training and performance optimization (e.g., PyTorch DDP, FSDP, DeepSpeed)
• Experience building tooling for runtime analysis, profiling, and performance diagnostics
• Experience with secure or privacy-constrained data environments (e.g., HIPAA-aware engineering practices)
• Experience working in interdisciplinary research areas such as climate, environment, health, or astrophysics

Benefits

• Generous paid time off including parental leave
• Medical, dental, and vision health insurance coverage starting on day one
• Retirement plans with university contributions
• Wellbeing and mental health resources
• Support for families and caregivers
• Professional development opportunities including tuition assistance and reimbursement
• Commuter benefits, discounts and campus perks