# Applied Research - Forward-Deployed

**Company:** [Prime Intellect](https://hotfix.jobs/companies/prime-intellect)
**Location:** San Francisco, CA
**Role:** Solutions Architecture
**Salary:** $150k – $300k/yr
**Skills:** Python, Hugging Face, Inference Engines, Agent Frameworks, Reinforcement Learning, Grpo, RLHF, Reward Modeling, Sft, Dspy, LangGraph, Mcp, Stagehand, Browser Automation, React
**Posted:** 2026-07-08

> Build and deploy custom agent environments, evaluation systems, and post-training workflows for strategic customers using Prime Intellect’s Lab platform. The role combines applied research, technical customer engagement, and production delivery across agentic AI, reinforcement learning, and LLM evaluation.

## Job Description

## Responsibilities
- Embed with strategic customers to understand agent architectures, failure modes, and product goals.
- Design custom reinforcement-learning environments, evaluation harnesses, verifiers, reward functions, and rubrics.
- Architect agent scaffolding for tool use, multi-step reasoning, memory, and sandbox execution.
- Configure and launch training runs on the Lab platform, iterating on rollout strategies and evaluation criteria.
- Lead technical engagements from discovery through deployed and improved models.
- Identify repeatable customer patterns and codify them into reference implementations, templates, documentation, and recipes.
- Provide customer feedback to shape the platform roadmap.
- Build examples and contribute to technical content, including blog posts, tutorials, and case studies.
- Develop evaluation methods for agentic behavior, including reasoning, tool-use correctness, failure recovery, and long-horizon task completion.
- Prototype agent harnesses for code generation, workflow automation, document processing, and other real-world tasks.
- Stay current on agentic AI, evaluations, and post-training methods.

## Requirements
- Hands-on experience building, evaluating, or deploying LLM-based agents.
- Strong evaluation-design skills, including measurement, rubric construction, and reward-signal assessment.
- Working knowledge of reinforcement learning and post-training concepts, including GRPO, RLHF, reward modeling, and SFT.
- Strong Python skills and familiarity with modern AI tooling.
- Experience with Hugging Face, inference engines, and agent frameworks.
- Experience in a customer-facing or consulting-oriented technical role, or as a technical founder.
- Excellent written and verbal communication skills.
- High agency and comfort working in ambiguous environments.

## Nice-to-Haves
- Experience with DSPy, LangGraph, MCP, Stagehand, or browser automation.
- Experience building or operating LLM evaluation pipelines at scale, including benchmarks, synthetic data generation, and model grading.
- Research experience, publications, open-source contributions, or ML/RL/agent benchmarks.
- Familiarity with sandbox and code-execution environments for agent evaluation.
- Web programming experience with React, TypeScript, or Next.js.

## Compensation and Benefits
- Cash compensation range: $150,000–$300,000, plus equity incentives.
- Flexible work options, including San Francisco or hybrid-remote arrangements.
- Visa sponsorship and relocation support.
- Professional development budget.
- Team off-sites and conference attendance.

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