Research​/Data Engineer – AI & Reinforcement Learning

Working hours: Full-time, approximately 9:00 AM–9:00 PM, 6 days per week

Visa: US citizens, green card holders, F-1, H-1B, O-1, and TN candidates considered

Our client is a cash-flow-positive, seed-stage AI company building high-quality, long-horizon reinforcement learning (RL) environments and post-training datasets for frontier AI laboratories.

The company actively works with leading US AI labs and top international players, helping advance the capabilities of next-generation AI models.

While traditional data providers often approach post-training data as a matter of increasing headcount, this team takes a research-driven approach. Its goal is to scale data production through self-improving processes, enabling increasingly sophisticated training environments without relying on linear increases in staffing.

You'll join a small, exceptionally strong team of researchers and engineers working at the intersection of machine learning research, data generation, and AI development. The company recently released a benchmark designed to measure how effectively models can iterate within an ML research loop.

The demand for the company’s post-training data exceeds its current production capacity. As a Research / Data Engineer, you’ll help scale the core team by combining hands‑on machine learning research with high-quality data production.

This role brings together two disciplines that are often separated: research and engineering. You’ll not only develop and experiment with ML techniques but also build the infrastructure, environments, and datasets required to improve frontier AI models.

The ideal candidate has a genuine research specialisation in areas such as reinforcement learning, model post‑training, evaluation, or interpretability, alongside strong engineering capabilities. You’ll take ownership of the process end to end, from provisioning infrastructure to designing realistic tasks and curating high-quality training data.

• Own post-training workflows: Manage the end‑to‑end process, from infrastructure provisioning and model post‑training to dataset curation and quality assurance.
• Build reinforcement learning environments: Design and develop sophisticated RL environments and long‑horizon tasks across multiple domains, including scientific and ML research workflows.
• Develop high‑quality training data: Create realistic, challenging, and valuable datasets that address the needs of leading AI research laboratories.
• Scale data generation: Build self‑improving developer processes and automated workflows that increase data production without simply adding more people.
• Drive research and experimentation: Apply machine learning research to improve post‑training methods, evaluation processes, and the quality of generated data.
• Maintain high data standards: Identify which tasks are realistic, economically valuable, and genuinely useful for advancing AI model capabilities.
• Build robust ML pipelines: Develop and optimise the engineering systems that support model training, experimentation, and data generation.

• Approximately 3–7 years of relevant experience in machine learning research, ML engineering, or a closely related field.
• Demonstrated research expertise in at least one area such as model post‑training, reinforcement learning, model evaluation, or interpretability.
• Strong engineering skills alongside research experience. Candidates must demonstrate both; excellence in only one area is not sufficient.
• Practical experience post‑training models and curating high‑quality post‑training datasets.
• Strong Python and PyTorch skills, with the ability to understand unfamiliar codebases quickly and solve…