Prime Intellect

Research Engineer

• Lead and participate in novel research to build a massive scale, highly reliable and secure decentralized training orchestration solution
• Optimize the performance, cost, and resource utilization of AI workloads by leveraging the most recent advances for compute & memory optimization techniques.
• Contribute to the development of our open-source libraries and frameworks for distributed model training.
• Publish research in top-tier AI conferences such as ICML & NeurIPS.
• Distill highly technical project outcomes in layman approachable technical blogs to our customers and developers.
• Stay up-to-date with the latest advancements in AI/ML infrastructure and tools, decentralized training research and proactively identify opportunities to enhance our platform's capabilities and user experience.

Research Engineer – Distributed Training

• Lead and participate in novel research to build a massive scale synthetic data generation pipeline and orchestration solution
• Optimize the performance, cost, and resource utilization of AI inference workloads by leveraging the most recent advances for compute & memory optimization techniques.
• Contribute to the development of our open-source libraries and frameworks for synthetic data generation and distributed RL frameworks.
• Publish research in top-tier AI conferences such as ICML & NeurIPS.
• Distill highly technical project outcomes in layman approachable technical blogs to our customers and developers.
• Stay up-to-date with the latest advancements in AI/ML infrastructure and tools, synthetic data gen research and proactively identify opportunities to enhance our platform's capabilities and user experience.

Research Engineer – Reinforcement Learning

• Build and optimize the systems infrastructure behind large-scale RL and distributed training workloads.
• 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.

Research Engineer – RL Infrastructure

AI Research Scientist

• Collaborate with leading experts in AI, distributed systems, and protocols to develop state-of-the-art open language models, coding agents, and scientific discovery models
• Contribute to projects focused on democratizing AI and making it universally accessible through the Prime Intellect platform
• Gain practical experience in developing and deploying large-scale AI models using novel architectures and distributed training techniques across thousands of GPUs and smaller clusters
• Publish research papers and present findings at top-tier AI conferences
• Develop a strong network within the decentralized AI community

AI Research Resident – Open Source AGI

Mechanical Engineer

• Design and implement interfaces across the platform covering compute orchestration and RL training
• Translate complex backend systems into intuitive, production-ready product experiences
• Own end-to-end product development from concept to deployment
• Build and maintain a scalable design system with reusable components
• Build for technical audiences: AI engineers and general software engineers
• Convert RL concepts into usable visual primitives and interactive workflows
• Implement platform developer tools including onboarding flows and debugging interfaces
• Create seamless interactions between frameworks and platform UI

Lead Design Engineer

• Design and build scalable RL training infrastructure — async trainers, environment orchestration, reward pipelines — across large GPU clusters.
• Optimize performance, cost, and resource utilization of RL workloads using state-of-the-art compute and memory optimization techniques.
• Contribute to our open-source libraries and frameworks for distributed RL training.
• Publish research at top-tier venues (ICML, NeurIPS).
• Write clear, approachable technical content distilling complex systems work for customers and the broader community.
• Stay current with advances in RL systems, distributed training, and ML infrastructure, and proactively identify opportunities to enhance our platform.

Research Engineer – RL Infra

Account Manager

• Develop and execute Prime Intellect’s global GPU sourcing strategy across H100/H200/B200-class infrastructure and beyond.
• Structure commercial agreements that balance cost, flexibility, term length, and growth optionality.
• Identify and evaluate infrastructure partners across hyperscalers, specialized AI clouds, data centers, colocation providers, and hardware vendors.
• Lead negotiations on pricing, SLAs, capacity reservations, expansion rights, and risk allocation.
• Continuously optimize blended gross margins through disciplined sourcing and contract structuring.
• Secure capacity for internal frontier RL research and model training.
• Coordinate closely with research and engineering teams to understand workload requirements (training vs inference vs long-context deployments).
• Align capacity planning with enterprise deployment roadmaps.
• Ensure compute supply keeps pace with customer expansion and new model launches.
• Work with infrastructure, platform, and DevOps teams to ensure partner capacity is onboarded efficiently and runs reliably in production.
• Track uptime, delivery timelines, and performance metrics across partners.
• Manage escalation paths and issue resolution with providers.
• Design playbooks for expanding from pilot deployments to large-scale clusters.
• Monitor global GPU supply dynamics, pricing trends, export constraints, and vendor roadmaps.
• Diversify supply to reduce concentration risk.
• Model long-term capacity requirements across revenue scenarios.
• Identify opportunities for structured capacity (e.g., reserved blocks, hybrid models, co-investment structures).
• Build long-term strategic relationships with infrastructure providers.
• Explore novel partnership models that create competitive advantage.
• Support cross-functional collaboration between Finance, Research, Sales, and Operations.
• Provide infrastructure insights for board and investor conversations.