Applied Research - Forward-Deployed
AI infrastructure company providing an integrated stack for training, evaluating, deploying, and continuously improving agentic models.
Funding history
Projects
About Prime Intellect
Prime Intellect, Inc. operates AI infrastructure spanning RL environments, hosted training and evaluations, inference, secure sandboxes, and globally sourced GPU compute.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will embed with strategic customers to understand their agent architectures, failure modes, and goals. You will design RL environments, evaluation harnesses, verifiers, and agent scaffolding; configure training runs; and lead engagements through deployed, improved models. You will turn field insights into reusable reference implementations, templates, documentation, and technical content. You will also develop evaluation methods, prototype agent harnesses, experiment with reward design, and apply current agentic AI, evaluation, and post-training practices to customer work.
Requirements
- Hands-on experience building, evaluating, or deploying LLM-based agents
- Evaluation design expertise
- Understanding of RL and post-training concepts including GRPO, RLHF, reward modeling, and SFT
- Strong Python skills and familiarity with Hugging Face, inference engines, or agent frameworks
- Customer-facing, consulting-adjacent technical, or technical founder experience
- Written and verbal communication skills
- High agency and comfort with ambiguity
Responsibilities
- Embed with strategic customers to understand agent architectures, failure modes, and product goals
- Design and build custom RL environments, evaluation harnesses, and verifiers
- Architect agent scaffolding for customer workflows
- Configure and launch training runs, iterating on reward functions, rollout strategies, and evaluation criteria
- Lead technical engagements from discovery through deployed, improved models
- Codify repeatable customer patterns into reference implementations, templates, and documentation
- Shape the platform roadmap with customer feedback
- Build examples and recipes for customers and open-source contributors
- Contribute technical content including blog posts, tutorials, and case studies
- Develop evaluation methodologies for agentic behavior
- Prototype agent harnesses for real-world tasks
- Experiment with reward design, rubric construction, and environment shaping
- Stay current on agentic AI, evaluation, and post-training methods
Benefits
- Equity incentives
- Flexible work
- Visa sponsorship
- Relocation support
- Professional development budget
- Team off-sites
- Conference attendance
