Senior Research Software Engineer

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Harvard University's Central Administration (CADM) is a 5,000+ employee organization that supports the university's overall excellence by understanding and serving the needs of its schools, students, faculty, staff, alumni, and surrounding communities. Through dynamic and collaborative partnerships, CADM provides high‑quality and efficient services to the schools to help them achieve their goals.

Design, plan, and implement software and data services that support and enrich research productivity and reliability. Develop software and data services with researchers to ensure that modern standards of reproducible research are kept.

The Harvard Data Science Initiative (HDSI) is hiring a Senior Research Software Engineer (RSE) to support a portfolio of faculty‑led research projects under the HDSI‑AWS Impact Computing Alliance. This role is designed for an engineer who thrives in research settings and enjoys translating scientific goals into robust, efficient and reproducible AI/ML systems.

Rather than being tied to a single lab, the RSE will provide shared, cross‑project engineering support—helping multiple teams accelerate discovery by building and optimizing machine learning infrastructure, improving performance on modern hardware (including AI accelerators), and enabling scalable execution in AWS and HPC environments. Projects may span domains such as climate and environmental science, global health, and other areas aligned with the alliance’s mission to deliver measurable social and environmental impact.

This is a hands‑on role with strong collaboration expectations: you’ll work directly with researchers, HDSI technical leadership, and the alliance team to deliver production‑grade research software and reusable technical patterns that benefit multiple projects across the Impact Computing umbrella.

This position is a benefits‑eligible, two‑year term appointment through June 30, 2028.

• 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
• Apply systems‑ and compiler‑level optimizations, including:
  • Loop transformations, vectorization, parallelization, and hardware‑specific tuning (e.g., SIMD)
• Implement and optimize kernels using CUDA, OpenMP, OpenCL, or accelerator‑specific programming models
• Contribute to or integrate with compiler and IR frameworks such as MLIR, LLVM, XLA, IREE, TVM, or Halide
• Analyze and improve performance using profiling and diagnostics focused on:
  • Latency, memory bandwidth, I/O throughput, and compute utilization
• Support execution in AWS cloud and HPC environments, including large‑scale model training, profiling, debugging, scaling, cost/performance tuning, reliability, CI/testing, packaging, deployment, reproducibility engineering
• Follow and promote modern ML and scientific software best practices, including:
  • Experiment tracking, reproducibility, version control, testing, packaging, and documentation
• Collaborate closely with faculty, researchers, and AWS consulting partners on systems engineering, performance optimization, ML infrastructure, compilers/framework integration, cloud/HPC execution.
• Communicate technical findings, tradeoffs, and progress clearly to research stakeholders (including documentation and handoff‑ready tooling)

• Occasionally required to work outside of normal business hours, and may be contacted during off‑hours
• Hybrid / primarily remote within approved payroll states

• 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…