Staff Research Engineer, Frontier AI, RL & Evaluation

As a Staff Research Engineer, you will investigate the capabilities, limits, and training methods of advanced AI systems. The work combines research and engineering, with a focus on careful experiments, reproducible results, high-quality data, and practical AI applications.

You will build research datasets, prototypes, tools, benchmarks, and evaluation systems for complex, multi-step workflows. You will also work with coding agents, computer-use and browser-use agents, and function-calling systems.

• Formulate research questions and design experiments that produce evidence-based conclusions.
• Train, test, and evaluate models using modern machine learning tools.
• Explore reinforcement learning, post-training, synthetic data generation, agentic systems, and model understanding.
• Develop environments and evaluations for coding agents, computer-use agents, browser-use agents, and tool-using systems.
• Analyze experimental results and turn validated technical findings into scalable AI applications.
• Collaborate with research, engineering, product, and operations stakeholders.
• Share findings through technical reports and other suitable communication channels.
• Support technical discussions, peer review, research collaboration, and mentoring.

This is an expert-level, part-time contractor role. The listing does not provide a pay rate.

• Time commitment: 20 or more hours per week.
• Location: Open worldwide.
• Language: Professional fluency in English.
• Education: A PhD or master's degree in artificial intelligence, machine learning, computer science, or a closely related technical field, or equivalent research experience.
• Experience: At least 7 years of professional experience, including significant research engineering work in machine learning or frontier AI systems.
• Technical skills: Strong Python programming and experience with modern artificial intelligence and machine learning frameworks.
• Research skills: Experience designing experiments, training or evaluating models, or developing AI systems.
• Work style: Strong judgment about experimental rigor and reproducibility, clear communication, independent execution, and cross-functional collaboration.
• Mentoring: Experience providing technical mentoring.
• Useful background: Synthetic or agentic data generation, reinforcement learning, post-training, model understanding, AI evaluation, benchmarks, agents, or tool-using systems.
• Additional useful experience: Coding-agent environments, user-interface or browser-use environments, function-calling systems, technical publications, open-source research, or major machine learning conferences.

Apply on OpenTrain with your resume, then complete the application on the hiring site.

AI training is the human work behind modern AI systems, including preparing data, testing models, rating outputs, and building evaluations. People with advanced technical expertise help make these systems more reliable by designing rigorous experiments and reviewing complex results.