Senior Research Engineer, Frontier Data
About this role
Turing is focused on accelerating superintelligence to drive economic progress by working with frontier AI labs. The company generates high-quality datasets, reinforcement learning environments, and research benchmarks to enhance model capabilities across various domains, including software engineering and enterprise knowledge work. Turing bridges the gap between frontier research and real-world deployment by using deployment signals to improve data, evaluations, and model performance.
This Senior Research Engineer role is central to building the infrastructure that powers this mission. The team develops large-scale datasets and reinforcement learning environments designed to evaluate and improve customer models on complex, multi-step workflows in high-value domains like Finance and Retail. Engineers will work on environments tailored for agents in areas such as software engineering, UI interaction, and general function-calling.
This position exists to advance the understanding of frontier AI systems by translating foundational research into practical, scalable applications. The ideal candidate will thrive at the intersection of research and engineering, driving innovation in areas like synthetic data generation, reinforcement learning, and model evaluation.
What you will do
- Investigate the capabilities, limitations, and training methods of frontier AI systems.
- Formulate research questions that inform Turing’s products, platforms, and technical strategy.
- Explore new approaches to synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation.
- Develop research-grade datasets, experiments, prototypes, and evaluation frameworks.
- Train, test, and evaluate models using modern AI and machine learning tools.
- Analyze results to draw evidence-based conclusions and establish rigorous experimental practices.
- Collaborate with cross-functional teams to translate research findings into product improvements.
What you need
- A PhD or Master’s degree in artificial intelligence, machine learning, computer science, or a closely related technical field is required.
- The candidate must possess a strong foundation in machine learning and practical experience designing experiments, training, or evaluating models.
- Demonstrated research experience in synthetic or agentic data generation, reinforcement learning, post-training, AI understanding, evaluation, or benchmarks is necessary.
- Strong programming skills are needed to implement, test, and iterate quickly within a research environment.
- Sound scientific judgment is required for interpreting complex research outcomes and guiding technical direction.
- Experience in research engineering is essential for building and iterating on research systems.
Nice to have
- Experience with agentic AI systems
Benefits
- $250,000 to $350,000 salary
Skills and tech stack
Questions candidates ask
What is the remote policy for this role?
The posting does not explicitly state the remote work policy for this position.
What is the compensation range for this role?
The salary range for this role is listed between $250,000 and $350,000.
What is the primary tech stack used by the team?
The role involves working with modern AI and machine learning tools for training and evaluation.
What kind of teams will I collaborate with?
Engineers will collaborate closely with Research, Engineering, Product, and Operations teams.