Research Engineer – RL Infrastructure

Prime Intellect

. Build and optimize systems infrastructure behind large-scale RL and distributed training workloads by contributing to the prime-rl framework .

Posted 10/16/2026full-timeRemote • California • United StatesMid-LevelSenior💰 $150,000 - $350,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and optimizing AI/ML infrastructure for large-scale model training, with a strong focus on performance optimization, distributed training frameworks, and systems engineering. Proficient in identifying bottlenecks and implementing improvements across compute, memory, and networking layers.

Highest-signal resume keywords

Systems Engineering ExperienceDeep Familiarity With PyTorchLarge-Scale Training TechniquesGPU Architecture UnderstandingPerformance Debugging

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Performance OptimizationCUDA ProgrammingTriton KernelsData ParallelismTensor ParallelismPipeline ParallelismDistributed Training FrameworksProfiling TechniquesRuntime OptimizationAsynchronous Training Pipelines

Soft Skills

Problem-SolvingCollaborationOwnershipAdaptabilityTechnical Writing

Tools & Technologies

PyTorch DistributedDeepSpeedFSDPMegatronVLLMRayMulti-Node GPU ClustersHigh-Performance NetworkingOpen-Source ContributionsCompiler Tooling

Industry Keywords

AI InfrastructureML SystemsDistributed TrainingRL Training InfrastructurePerformance Debugging

Tech Stack

Tools & technologies

Node.jsPyTorchRay

About the role

Key responsibilities & impact

Build and optimize systems infrastructure behind large-scale RL and distributed training workloads by contributing to the 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 the 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 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

Requirements

What you’ll need

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
Experience writing or optimizing CUDA / Triton kernels (optional)
Experience with compiler or runtime optimization for ML systems (optional)
Experience working on RL training infrastructure, rollout systems, or asynchronous training pipelines (optional)
Experience with multi-node GPU clusters and high-performance networking (optional)
Contributions to open-source ML systems or infrastructure projects (optional)
Interest in publishing technical work or sharing insights through engineering blogs and technical writing (optional)

Benefits

Comp & perks

Equity incentives included in cash compensation range
Flexible work arrangements, with the option to work remotely or in-person at offices in San Francisco
Visa sponsorship
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