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Member of Technical Staff - Pre-Training Infra

Reflection· Research
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San Francisco, CA, London, New York, NYFull-timePosted 7mo ago

About the role

Role focused on building and scaling distributed training systems for frontier model pre-training, collaborating with research teams to design, operate, and optimize large-scale training runs and infrastructure.

Responsibilities

  • Build and scale distributed training systems that power frontier model pre-training.
  • Work closely with research teams to design and operate large-scale training runs for foundation models.
  • Develop infrastructure that enables efficient training across thousands of GPUs using modern distributed training frameworks.
  • Optimize training throughput, stability, and efficiency for large model training workloads.
  • Collaborate directly with pre-training researchers to translate experimental ideas into scalable, production-ready training systems.
  • Improve performance of distributed training workloads through optimization of communication, memory usage, and GPU utilization.
  • Build and maintain training pipelines that support large-scale datasets, checkpointing, and experiment iteration.
  • Debug and resolve performance bottlenecks across distributed training stacks including model parallelism, GPU communication, and training runtime systems.
  • Contribute to the development of systems that enable rapid experimentation and iteration on new training techniques.

Qualifications

  • Experience building or operating distributed training systems for large machine learning models.
  • Strong experience working with modern distributed training frameworks such as Megatron, DeepSpeed, or similar large-scale training systems.
  • Familiarity with large-scale model parallelism strategies (data, tensor, pipeline, or expert parallelism).
  • Experience optimizing training throughput and GPU utilization in large distributed environments.
  • Familiarity with GPU communication libraries such as NCCL and performance tuning for distributed workloads.
  • Experience working closely with ML researchers to productionize experimental training workflows.
  • Strong debugging skills across GPU compute, distributed training systems, and large-scale ML pipelines
  • Experience working with large datasets and training pipelines used for foundation model pre-training.

Skills

Required:Distributed training systemsModern distributed training frameworksModel parallelismGPU performance optimization

Benefits

  • Top-tier compensation: Salary and equity structured to recognize and retain our talent globally.
  • Stock options: Everyone who joins and contributes to Reflection's success gets to share in the upside through stock options.
  • Health & wellness: Comprehensive medical, dental, vision, and life, with an annual wellness allowance.
  • Meals: Lunch and dinner are provided in the office daily.
  • Life & family: 22 weeks paid parental leave for all new birthing and non-birthing parents, including adoptive and surrogate journeys.
  • Vacation days: Unlimited paid time off in the U.S. and 30 days in the U.K.
  • Sponsorship support: We sponsor visas to help exceptional talent join our team and support long-term immigration pathways where applicable.
  • Team building: We have regular off-sites, happy hours, and team celebrations.
Last updated Oct 1, 2026. Interested? Apply on the company site.

About Reflection

Building frontier open intelligence

Building frontier open intelligence.

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