Primeintellect

Research Engineer - RL Infrastructure

San Francisco

Sponsorship not specified$150k-$350kDetected 14 days ago

Node.jsFull-Stack DevelopmentDatabricksDatadogMachine LearningPyTorchSystems EngineeringResearchCommunication

About the role

The next generation of AI companies, enterprises, and research teams do not just need more GPUs.
Its spans the full stack of training, deploying and continuously improving models - compute, large-scale RL, environments, sandboxes, evals, and deployment.
WHY THIS ROLE MATTERS The next frontier in AI will not be unlocked by models alone.

the infrastructure frontier AI labs build internally, made available to every ambitious AI team.
Visa sponsorship and relocation support for international candidates.
If you're excited about building the systems foundation for frontier-scale RL and open superintelligence, we'd love to hear from you.

Strong systems engineering experience in AI/ML infrastructure, especially around large-scale model training or inference.
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.
Comfort working in a fast-moving environment with ambiguous problems and high ownership.
Experience writing or optimizing CUDA / Triton kernels.
Experience with compiler or runtime optimization for ML systems.
Experience working on RL training infrastructure, rollout systems, or asynchronous training pipelines.
Experience with multi-node GPU clusters and high-performance networking.

Cash Compensation Range of $150-350k, plus equity.
Flexible work arrangements, with the option to work remotely or in person from our San Francisco office.
Quarterly team offsites, hackathons, conferences, and learning opportunities.

Our platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models.

This listing is sourced directly from Primeintellect's careers page and normalized into a canonical job model.