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Salary: USD 150,000 - 300,000 / annual
Prime Intellect is building an open superintelligence stack—infrastructure that frontier AI labs build internally, now available to every ambitious AI team. The platform unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment for post-training at frontier scale, from SFT and RL to tool use, agent workflows, and continuously improving production models.
In this Applied Research role, you will own RL programs end-to-end: from understanding ambiguous customer problems through deployed improvement loops. You will work directly with customer researchers, engineers, and leaders, translating workflows into tasks, environments, evaluations, and reward signals. Your responsibilities include running post-training experiments against real baselines, deploying results, and determining what generalizes into Prime's shared stack.
Key responsibilities:
- Technical and business discovery to produce clear research plans, success measures, environments, and baselines
- RL, distillation, and other post-training experiments tied to deployment outcomes
- Building agent systems handling tool use, memory, recovery, long horizons, and human oversight
- Deployment and adoption decisions; converting recurring parts into reusable environments, recipes, data loops, or platform capabilities
- Working across active programs in technology, science, engineering, regulated industries, and the public sector
You will separate model limitations from task, harness, inference, and infrastructure failures. When results look good, you'll determine whether gains came from the model or from data, harness, inference, or infrastructure. Reusable parts become shared research, open-source systems, and product capabilities.
REQUIREMENTS:
- Relevant experience in customer-embedded technical work, research engineering, technical founding, applied ML consulting with real build ownership, or end-to-end post-training programs
- Comfort with senior external teams; ability to connect experimental evidence to business decisions without lowering research standards
- Hands-on experience building and debugging model-training, post-training, agent, evaluation, or closely adjacent experimental loops
- Strong experimental judgment: meaningful baselines, careful trace inspection, clear failure attribution, willingness to change direction based on evidence
- Strong Python; ability to build artifacts that other researchers and engineers can run, inspect, and extend
- Comfort owning ambiguous problems and keeping partners and teammates current as you resolve them
- Evidence of exceptional work valued: publications, open-source systems, benchmarks, shipped products, or difficult projects demonstrating how evidence changed your approach
