Hiring nowDistributed training and research systems

Research Productivity Engineer

Also called Software Engineer, Research Productivity

A Research Productivity Engineer builds the tools and automation that let researchers run experiments, analyse results and reproduce them faster. It matters because research speed depends on removing friction, not only on adding compute.

What they do

They watch how researchers actually work and find where time is lost. It might be slow job launches, brittle configs, unclear run lineage or analysis done by hand. They build tools to fix it: launch wrappers, experiment trackers, comparison dashboards, templates and automated checks. They profile jobs, tune scheduler use and make failures easier to diagnose. They write documentation, run short training sessions and collect feedback. They also decide what not to build, and retire tools that nobody uses. Much of the job is sitting with researchers and watching them work.

What a strong hire delivers

In the first months, a strong hire maps where researchers lose time and ships fixes for the worst problems first. Runs become easier to launch, track and repeat. Results can be traced back to code, data and config. Researchers adopt the tools by choice. The team can show a shorter path from hypothesis to a result others can reproduce.

How you can tell they are good

Ask them to walk through a tool they built and who used it. Strong candidates describe the researcher's problem first, then the design. They can show how they measured the change. Ask how they handled a researcher who refused to switch. Worry if they talk only about technology, or cannot say what got faster.

Why it is hard to fill

The title varies between companies, so searching by it misses people. The work sits between infrastructure engineering and research, and few engineers enjoy both. Many strong candidates are doing it under another name, such as ML platform or training infrastructure. Others are researchers who built tooling by necessity. Candidates need systems depth, but also patience and good taste in developer experience, which is a rare mix.

Where to find them

Look at engineers from ML platform, training infrastructure and high-performance computing teams. Distributed systems engineers who moved close to research users are another source. Some come from frontier labs, research-heavy start-ups and large technology companies with internal ML platforms. Others are research engineers who spent more time on tooling than on models. Developer experience engineers with GPU or scheduler exposure can also fit.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for a Research Productivity Engineer

  1. Tell me about a research tool you built. Who used it and what changed for them?

    A strong answer names the users, the pain and a measured change, such as fewer failed launches or faster comparisons. They mention adoption and feedback. Worry if they describe features without users or evidence of impact.

  2. How would you make an experiment reproducible when code, data and config all change between runs?

    They cover versioning code, data and config, capturing environment and seeds, and recording lineage automatically. They accept that some nondeterminism remains and say how to bound it. Worry if they rely on researcher discipline alone.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are hiring a Research Productivity Engineer to help our researchers go from idea to trustworthy result faster. You will work directly with researchers, find where time is lost in running and analysing experiments, and build the tools and automation that fix it.

What you will do

  • Build and maintain tools for launching, tracking and comparing experiments
  • Automate repetitive research workflows, from setup to analysis
  • Make results reproducible by capturing code, data, config and environment
  • Profile training jobs and improve scheduler and cluster usage with the infrastructure team
  • Improve error messages, logging and diagnosis for failed runs
  • Gather feedback from researchers and decide what to build, change or retire
  • Write clear documentation and help researchers adopt new tools

What you will bring

  • Built tools used by researchers or engineers, with evidence of adoption
  • Solid experience with distributed systems, high-performance computing or ML systems
  • Hands-on work with job schedulers and cloud orchestration
  • Comfortable profiling GPU workloads and finding bottlenecks
  • Strong software engineering habits, including testing and clear interfaces
  • Able to work closely with researchers and take their feedback well

Nice to have

  • Experience with distributed training frameworks
  • Experience building experiment tracking or analysis tooling
  • Background in research or research engineering

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Distributed systemsAdvancedDesign reliable work across networked machines
  • GPU programmingAdvancedOptimise parallel accelerator kernels
  • Cloud and orchestrationWorkingProvision and operate scalable compute services
  • Research operationsWorkingCoordinate research resources and reproducible execution

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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