Engineering and infrastructure

Distributed training and research systems

Distributed training and research systems covers the engineers who run large training jobs across many accelerators and keep research infrastructure reproducible. AI companies need them so researchers can test ideas on real hardware without losing work to avoidable failures.

Training runs are bigger, longer and more expensive to get wrong, so teams feel every stalled job and every result nobody can repeat. Founders often hire researchers first, then find the surrounding systems are fragile. The tricky part is fit. Good candidates come from distributed systems, high-performance computing or ML systems, and each brings different instincts. Look for people who respect that the research team owns the model questions.

Hiring now 3Emerging 1

Roles in this family

4 roles

Hiring nowDistributed Training EngineerA Distributed Training Engineer scales model training across many accelerators while keeping results correct. They keep expensive training runs…Hiring nowResearch Infrastructure EngineerA research infrastructure engineer gives researchers dependable compute and software environments, so experiments run, repeat and finish without…EmergingTraining Reliability EngineerA Training Reliability Engineer keeps long training runs alive and trustworthy. They detect failures, recover quickly and prove the recovered run is…Hiring nowResearch Productivity EngineerA Research Productivity Engineer builds the tools and automation that let researchers run experiments, analyse results and reproduce them faster. It…

Where the people come from

Distributed systems, high-performance computing or ML systems

Not to be confused with. Core model hypotheses remain owned by research teams.

Core skills

  • Distributed systems
  • GPU programming
  • Cloud and orchestration
  • Research operations

Where it is used

  • Cross-sector AI platforms
  • Semiconductors and compute infrastructure

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