What are the responsibilities and job description for the Forward Deployed Research Engineer position at Clera?
About The Role
We're a fast-growing, early-stage AI infrastructure company (11–50 people, founded 2025, headquartered in San Francisco) building the platform for creating, training, and evaluating AI models within Reinforcement Learning (RL) environments. Our tooling helps AI labs and enterprises generate high-quality post-training datasets that align AI systems to real-world workflows — at scale.
We're looking for a Forward Deployed Research Engineer to sit at the intersection of cutting-edge AI research and hands-on customer work. You'll embed directly with customers, understand their technical challenges, and build solutions on top of our platform that make their AI training and evaluation pipelines dramatically better.
What You'll Do
Required
Location
Based in San Francisco, CA. This is an on-site or hybrid role at our San Francisco headquarters.
We're a fast-growing, early-stage AI infrastructure company (11–50 people, founded 2025, headquartered in San Francisco) building the platform for creating, training, and evaluating AI models within Reinforcement Learning (RL) environments. Our tooling helps AI labs and enterprises generate high-quality post-training datasets that align AI systems to real-world workflows — at scale.
We're looking for a Forward Deployed Research Engineer to sit at the intersection of cutting-edge AI research and hands-on customer work. You'll embed directly with customers, understand their technical challenges, and build solutions on top of our platform that make their AI training and evaluation pipelines dramatically better.
What You'll Do
- Work directly alongside customers (AI labs, research teams, enterprises) to deploy and customize RL environments for their specific use cases.
- Build, configure, and extend evaluation environments, verifiers, and reward signals using our Environment SDK and Training Eval Platform.
- Diagnose and debug agent behavior in live customer environments, leveraging failure analysis tooling and automated QA agents to surface root causes.
- Tune reward signals and grader logic to improve alignment between prompts, tasks, and model outcomes.
- Identify patterns across customer deployments to surface product insights and feed them back into the core platform roadmap.
- Run large-scale agent task executions across thousands of concurrent environments and manage/analyze the resulting taskset data.
- Collaborate closely with internal research and engineering teams to translate field learnings into platform improvements.
Required
- Strong software engineering fundamentals — comfortable writing production-quality Python.
- Hands-on experience with machine learning and/or AI model training workflows.
- Familiarity with Reinforcement Learning concepts (environments, reward functions, agents, evaluation loops).
- Ability to work directly with customers in a technical capacity — explaining complex systems clearly and building trust quickly.
- Strong debugging instincts: you enjoy getting to the bottom of hard, ambiguous problems.
- Comfortable operating in a fast-paced, early-stage environment with significant autonomy.
- Prior experience in a forward-deployed, solutions engineering, or field research role.
- Familiarity with post-training data pipelines, RLHF, or model alignment techniques.
- Experience building or evaluating LLM/agent-based systems.
- Background in AI evaluation frameworks or dataset curation at scale.
- Competitive salary and equity commensurate with experience at an early-stage AI startup.
- Opportunity to work on some of the most technically challenging problems in AI alignment and RL infrastructure.
- Small, high-ownership team — your work directly shapes the product and company direction.
Location
Based in San Francisco, CA. This is an on-site or hybrid role at our San Francisco headquarters.