[Hiring] Applied Research - Forward-Deployed RL @Prime Intellect

Role Description

You will own applied RL programs from the first ambiguous customer problem through a deployed improvement loop. You will understand the workflow and business stakes, decide what can be measured and learned, build the environment and evaluation, run post-training experiments, deploy the result, and determine what should generalize into Prime's shared stack. The majority of your work will be organized around external programs. You will work directly with customer researchers, engineers, and leaders, and you will be accountable for both the research result and its practical impact.

Responsibilities

• Turn a workflow into tasks, environments, evaluations, and reward signals.
• Run post-training experiments against real baselines and deploy what works.
• Determine whether gains come from the model or from data, harness, inference, or infrastructure.
• Convert reusable parts into shared research, open-source systems, and product capabilities.
• Work directly with customers, users, or research partners while maintaining a substantive research agenda.

Role scope

• Active programs across technology, science, engineering, regulated industries, and the public sector.

Representative early problems

• Turn a partially documented workflow and business goal into an environment, baseline, and evaluation that the customer team agrees measures the right outcome.
• Improve a domain agent through RL or another post-training method while separating model limitations from task, harness, inference, and integration failures.
• Carry a successful experiment into deployment, measure its effect on the customer outcome, and convert the recurring parts into reusable environments, recipes, data loops, or platform capabilities.

What you will own

• Technical and business discovery that produces a clear research plan, success measure, environment, and baseline.
• RL, distillation, and other post-training experiments tied to deployment outcomes.
• Agent systems that handle tool use, memory, recovery, long horizons, and human oversight.
• Deployment and adoption decisions, plus reusable artifacts carried back to Prime's research, engineering, and product teams.

Qualifications

• Relevant experience: customer-embedded technical work, research engineering, technical founding experience, applied ML consulting with real build ownership, or end-to-end post-training programs.
• Comfortable with senior external teams and able to connect experimental evidence to business decisions without lowering the research bar.
• 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, and a willingness to change direction when the evidence changes.
• Strong Python skills and the 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: publications, open-source systems, benchmarks, and shipped products.

Benefits

• Cash Compensation Range of $150-300k + equity incentives.
• Flexible Work (San Francisco, New York, London, or remote).
• Visa Sponsorship & relocation support.
• Professional Development budget.
• Team Off-sites & conference attendance.