Research Engineer - Distributed Training
Prime Intellect builds the 'Open Superintelligence Stack' — an integrated compute, training, inference, and sandbox platform that lets companies train, deploy, and continuously improve their own AI models and agents. It serves AI startups, 'neolabs', and enterprises (over 6,000 customers, including Ramp and Zapier) that want to own their model optimization loop rather than rely solely on closed frontier labs.
Maintainer signals as of 8/12/2026
Funding history
Investors
Projects
About Prime Intellect
Prime Intellect is a San Francisco-based AI infrastructure company building what it calls the Open Superintelligence Stack: a full-stack platform spanning GPU compute (on-demand and reserved clusters), large-scale reinforcement learning training ('Lab'), an Environments Hub with 2,500+ community RL environments, hosted evaluations, sandboxed code execution, and dedicated/serverless model inference with native LoRA support. The company maintains open-source libraries (verifiers and prime-rl) used to build and train RL environments, and publishes frontier open research such as the INTELLECT and SYNTHETIC model/dataset series. Prime Intellect works with AI startups, enterprises, and 'neolab' customers such as Ramp and Zapier, helping them turn production traces and evaluations into custom-trained, post-trained agent models that outperform closed frontier models on specific workflows at lower cost and latency. The company has raised over $150M in total funding, including a $130M Series A led by Radical Ventures with participation from NVIDIA Ventures, Intel Capital, and Dell Technologies Capital, and reports over $100M in annualized revenue.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will build and optimize distributed training infrastructure for pre-training and large-scale reinforcement learning workloads. You will improve efficiency across compute, memory, networking, and scheduling, implement low-level optimizations, develop distributed training systems, and collaborate with researchers on frontier-scale model training.
Requirements
- AI/ML infrastructure engineering experience
- Large-scale model training or inference experience
- PyTorch
- PyTorch Distributed
- DeepSpeed
- FSDP
- Megatron
- vLLM
- Ray
- Training performance optimization
- Data parallelism
- Tensor parallelism
- Pipeline parallelism
- GPU architecture
- Profiling
- Performance debugging
- CUDA
- Triton
- Compiler optimization
- Runtime optimization
- RL training infrastructure
- Multi-node GPU clusters
- High-performance networking
- Open-source contributions
Responsibilities
- Build and optimize distributed training infrastructure
- Improve training efficiency across compute, memory, networking, and scheduling layers
- Design and implement kernel, communication path, and runtime optimizations
- Develop distributed training systems for data, tensor, and pipeline parallel workloads
- Shape the architecture of the RL training stack
- Contribute to open-source libraries and internal infrastructure
- Translate system bottlenecks into concrete improvements
- Track advances in training systems, inference systems, compiler tooling, runtime tooling, and hardware-aware optimization
Benefits
- Equity incentives
- Flexible work arrangements
- Remote or in-person work options
- Visa sponsorship
- Relocation assistance
- Quarterly team off-sites
- Hackathons
- Conferences
- Learning opportunities