Amadeus Search
Role:
Senior Research Engineer Employment Type:
Full-time Location:
On-site, San Francisco (5 days/week, no remote option) Compensation:
$200,000 - $275,000 base salary + 0.5-2% equity About the Company
This YC-backed startup is focused on post-training data and reinforcement learning (RL) environments for foundation model labs and applied AI companies. The company has raised ~$3.6M from top-tier venture capital firms and is experiencing rapid growth (200%+ month-over-month). With a small, research-focused team, they are building scalable data and RL infrastructure to power next-generation AI development. The Role
The Senior Research Engineer will be responsible for designing scalable RL recipes, building modular environments, and driving research at the intersection of infrastructure and reinforcement learning. This role includes working on both internal and customer-facing projects, with opportunities to publish foundational research, open-source environments, and training data.
The position requires a balance of hands-on implementation, research innovation, and collaboration with product teams to create user-friendly interfaces for data generation and evaluation. Key Responsibilities Design and implement scalable RL recipes for task-specific post-training models. Develop modular environments, reward functions, and evaluator scaffolds. Build data generation and curation pipelines to support post-training workflows. Conduct foundational research and contribute to open-source publications. Collaborate with product teams to create accessible tools for non-technical users. Advance research bridging scalable infrastructure and modern RL frameworks. Candidate Requirements Master's or PhD in Computer Science or a related field. Strong experience with
PyTorch
and modern RL tooling. Familiarity with post-training techniques (e.g., GRPO, reward engineering). Experience in evaluations and scalable training environments. Strong publication record in top-tier venues (ICLR, NeurIPS, ICML, etc.). Ability to thrive in a small, fast-paced, and research-driven environment. Tech Stack
Python, PyTorch, reinforcement learning frameworks. Culture & Opportunity Cutting-edge research : Work on novel, unpublished training environments. Direct lab exposure : Projects are validated and used in production by leading AI labs. High autonomy : Freedom to propose, design, and run experiments with minimal oversight. Early team impact : Join as one of the first 10 employees with significant equity upside. Benefits & Perks Competitive salary ($200K-$275K) + 0.5-2% equity. Visa sponsorship available. On-site, collaborative work environment in San Francisco. Early-stage opportunity to shape product direction and research focus.
Senior Research Engineer Employment Type:
Full-time Location:
On-site, San Francisco (5 days/week, no remote option) Compensation:
$200,000 - $275,000 base salary + 0.5-2% equity About the Company
This YC-backed startup is focused on post-training data and reinforcement learning (RL) environments for foundation model labs and applied AI companies. The company has raised ~$3.6M from top-tier venture capital firms and is experiencing rapid growth (200%+ month-over-month). With a small, research-focused team, they are building scalable data and RL infrastructure to power next-generation AI development. The Role
The Senior Research Engineer will be responsible for designing scalable RL recipes, building modular environments, and driving research at the intersection of infrastructure and reinforcement learning. This role includes working on both internal and customer-facing projects, with opportunities to publish foundational research, open-source environments, and training data.
The position requires a balance of hands-on implementation, research innovation, and collaboration with product teams to create user-friendly interfaces for data generation and evaluation. Key Responsibilities Design and implement scalable RL recipes for task-specific post-training models. Develop modular environments, reward functions, and evaluator scaffolds. Build data generation and curation pipelines to support post-training workflows. Conduct foundational research and contribute to open-source publications. Collaborate with product teams to create accessible tools for non-technical users. Advance research bridging scalable infrastructure and modern RL frameworks. Candidate Requirements Master's or PhD in Computer Science or a related field. Strong experience with
PyTorch
and modern RL tooling. Familiarity with post-training techniques (e.g., GRPO, reward engineering). Experience in evaluations and scalable training environments. Strong publication record in top-tier venues (ICLR, NeurIPS, ICML, etc.). Ability to thrive in a small, fast-paced, and research-driven environment. Tech Stack
Python, PyTorch, reinforcement learning frameworks. Culture & Opportunity Cutting-edge research : Work on novel, unpublished training environments. Direct lab exposure : Projects are validated and used in production by leading AI labs. High autonomy : Freedom to propose, design, and run experiments with minimal oversight. Early team impact : Join as one of the first 10 employees with significant equity upside. Benefits & Perks Competitive salary ($200K-$275K) + 0.5-2% equity. Visa sponsorship available. On-site, collaborative work environment in San Francisco. Early-stage opportunity to shape product direction and research focus.