Research Engineer – RL Infrastructure

Job not on LinkedIn

🔥 8 minutes ago

🏄 California – Remote

🏄 California – Remote

💵 $150k - $350k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

📚 Research Engineer

👻 Ghost score 0%

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Prime Intellect

WebsiteLinkedInAll Job Openings

1 - 10 employees

🤖 Artificial Intelligence

☁️ SaaS

Artificial Intelligence • SaaS • Cloud Computing

Prime Intellect is a company focused on democratizing AI development by providing scalable and decentralized computing resources for training models. Their platform allows users to find and share global compute resources, enabling the training of state-of-the-art models through distributed clusters. They promote the collective ownership of AI innovations, including language and scientific models. Prime Intellect also offers a range of GPU options to facilitate affordable and efficient model training. They aim to advance decentralized training research and open-source AI development on a global scale.

📋 Description

• 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

• 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

• 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