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Research Engineer - Distributed Training

Prime Intellect· Research
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San Francisco, Remote$150k - $350kFull-timePosted 11mo ago

About the role

Prime Intellect is building an open superintelligence stack and a full-stack platform for frontier-scale training, evaluation, and deployment. The role focuses on building and optimizing distributed training infrastructure for pre-training and large-scale RL workloads, contributing to the open-source prime-rl framework, and shaping the RL training stack.

Responsibilities

  • Build and optimize the distributed training infrastructure behind our pre-training and large-scale RL training workloads by contributing to our prime-rl framework.
  • Improve end-to-end training efficiency across compute, memory, networking, and scheduling layers.
  • Design and implement low-level performance optimizations, including kernels, communication paths, and runtime improvements.
  • Work on distributed training systems spanning data, tensor, and pipeline parallel workloads.
  • Help shape the architecture of our RL training stack, including async rollout and post-training systems.
  • Contribute to open-source libraries and internal infrastructure used for frontier-scale model training.
  • Collaborate closely with researchers and infrastructure engineers to translate bottlenecks into concrete systems improvements.
  • Stay at the frontier of training systems, inference systems, compiler/runtime tooling, and hardware-aware optimization techniques.

Qualifications

  • Strong systems engineering experience in AI/ML infrastructure, especially around large-scale model training or inference.
  • Deep familiarity with PyTorch and distributed training frameworks such as PyTorch Distributed, DeepSpeed, FSDP, Megatron, vLLM, Ray, or related tooling.
  • Experience optimizing training performance across kernels, memory movement, communication overhead, or parallelization strategy.
  • Hands-on experience with large-scale training techniques including data parallelism, tensor parallelism, and pipeline parallelism.
  • Strong understanding of GPU architecture, profiling, and performance debugging.
  • Ability to identify bottlenecks across the stack and drive improvements from first principles.
  • Comfort working in a fast-moving environment with ambiguous problems and high ownership.

Skills

Required:AI/ML infrastructure systemsPyTorch and distributed training frameworksPerformance optimizationLarge-scale training techniquesGPU architecture & debuggingBottleneck identificationAdaptability in fast-moving environments

Benefits

  • Cash Compensation Range of $150-350k, including equity incentives, aligning your success with the growth and impact of Prime Intellect.
  • Flexible work arrangements, with the option to work remotely or in-person at our offices in San Francisco.
  • Visa sponsorship and relocation assistance for international candidates.
  • Quarterly team off-sites, hackathons, conferences and learning opportunities.
  • Opportunity to work with a talented, hard-working and mission-driven team, united by a shared passion for leveraging technology to accelerate science and AI.
Last updated Oct 21, 2026. Interested? Apply on the company site.

About Prime Intellect

Commoditizing Compute & Intelligence

Prime Intellect democratizes AI development at scale. Our platform makes it easy to find global compute resources and train state-of-the-art models through distributed training across clusters. Collectively own the resulting open AI innovations, from language models to scientific breakthroughs.

AI developmentbessemer-ai-infrastructure-2026compute resourcesdistributed trainingmodelsreinforcement-learning-platformsscience
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