AI Research Engineer

Revelo is the data lab specializing in code.

Since 2023, Revelo has been building RL environments, evals and specialized mid- to post-training data for leading AI labs. We help them train and evaluate their models on everything LLMs do through code: agentic workflows, software engineering, cybersecurity, terminal work, web apps, games, 3D graphics, infrastructure, ML engineering and scientific computing. Our tasks draw on human expertise from a network of 400,000+ engineers that we have vetted for more than ten years.

We are growing our AI research team

As an AI Research Engineer, you will design tooling, evals, RL environments, datasets, benchmarks, and experimental tools to improve how models are trained and evaluated. This individual-contributor role combines applied work with original frontier research. We are hiring multiple individuals for this position to grow our team.

Apply if you want to collaborate directly with AI research teams from Anthropic · Google DeepMind · Meta · Amazon · Mistral · Tencent · ByteDance

Core responsibilities

Applied research

• Turn high-level, bespoke data requests from AI labs into workable, high-quality samples. Work directly with research teams from leading AI labs to get feedback and advance your work.
• Review and adapt relevant research, build prototypes, and test sample quality and feasibility. Use evidence and feedback to refine the approach quickly.
• Work closely with delivery, engineering, our CTO and CEO to bring your research to the real world at scale.

Frontier research

• Proactively identify gaps in model capabilities beyond explicit lab requests. Build benchmarks and datasets that expose those gaps and support further research.
• Conduct fundamental research on training and evaluation, including how models exploit weaknesses in reinforcement learning gyms and how to prevent that behavior.
• Design reproducible experiments, analyze results, and author research papers and technical reports for publication.

Qualifications

Required

• Hands-on AI engineering experience building or evaluating LLM-based systems, integrating models with tools + data sources, and debugging model behavior.
• Experience with academic research, for example through a master’s or PhD project or a research assistantship.
• Ability to read relevant research and translate it into working prototypes within short delivery cycles.
• Sound experimental judgment, including selecting baselines and metrics, analyzing model failures, and producing reproducible results.
• Clear technical writing and effective collaboration with researchers, engineers, and delivery teams to resolve ambiguous requirements.
• Experience building datasets, benchmarks, or data pipelines with systematic quality checks.

Preferred

• Experience with LLM post-training, reinforcement learning, or agent environments, including reward hacking and robustness testing.
• Work on code generation, software engineering, or multimodal tasks.
• Research publications, technical reports, or open-source contributions that demonstrate original experimentation.

Cool stuff the research team is doing

RL environments

Work directly with the Harbor team to advance the standards in RL data and mitigate reward hacking.

Code Index

Our live leaderboard that ranks frontier models on private task collections that are never published.

Behavioral collections

Studies on data driving behavior. From “lying and lazy LLMs” to “what makes games fun?”.

AAA games

Procedural loops, 3D modeling gyms and PRD eng to teach small models to create sick games.

Hop on a plane

(Ok, not that cool.) Go hang out and learn from researchers at our clients/NeurIPS/ICML.

Multi-modal workflows

Improve how agents perform on poorly-defined tasks across video, audio and code.