About this role
The day-to-day work for a Senior Research Engineer at Turing involves advancing the understanding of frontier AI systems and developing practical innovations across data, algorithms, evaluation, and training. This role centers on building large-scale datasets and reinforcement learning (RL) environments that power post-training for leading AI labs and enterprises. Engineers will create RL environments designed to evaluate and enhance customer models across complex, multi-step workflows in high-value domains like Finance and Retail. They will investigate cutting-edge areas such as synthetic and agentic data generation, model understanding, and benchmarks.
This position exists to bridge the gap between frontier AI research and real-world enterprise deployment. The engineer contributes to closing the loop between research findings and tangible improvements in model capabilities. This involves translating theoretical insights into scalable applications for Turing’s platforms and products. The ideal candidate possesses a strong ability to operate at the intersection of deep research and robust engineering practices.
A successful candidate thrives in an environment that demands rigorous experimentation and clear communication across technical and cross-functional teams. They should be someone who can move fluidly from formulating high-impact research questions to building research-grade prototypes and ensuring those prototypes translate into practical, impactful product enhancements.
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 in synthetic and agentic data generation, reinforcement learning, post-training, model understanding, and evaluation.
- Develop research-grade datasets, experiments, prototypes, and evaluation frameworks.
- Train, test, and evaluate models utilizing modern AI and machine learning tools.
- Analyze experimental results to draw clear, evidence-based conclusions.
- Establish sound practices for experimental rigor, data quality, reproducibility, and interpretation.
- Collaborate with Research, Engineering, Product, and Operations teams to implement research findings.
What you need
- A PhD or Master’s degree in artificial intelligence, machine learning, computer science, or a closely related technical field is required.
- Possesses a strong foundation in machine learning and practical experience designing experiments, training, or evaluating models.
- Demonstrated research experience is necessary in areas such as synthetic or agentic data generation, reinforcement learning, or post-training.
- Requires strong programming skills and the ability to implement, test, and iterate quickly within a research setting.
- Must possess scientific judgment regarding experimental rigor and interpretation of results.
- Experience in developing research-grade tooling and evaluation frameworks is essential.
- The candidate must be able to clearly communicate complex technical findings to diverse audiences.
Nice to have
- Experience with UI-Environments for Computer-Use/Browser-Use agents
Benefits
- $250,000 to $350,000 salary
Skills and tech stack
Questions candidates ask
What is the typical compensation range for this role?
The salary offered for this position ranges from $250,000 to $350,000.
What kind of research is involved in this role?
The work involves investigating the capabilities and limitations of frontier AI systems. Engineers will explore new approaches in synthetic and agentic data generation, reinforcement learning, and model understanding.
How is the work structured across teams?
The role requires close collaboration with Research, Engineering, Product, and Operations teams. This collaboration is necessary to translate research findings into improvements for Turing’s products and platforms.
What is the focus of the technical environment?
The environments created vary based on the model capability being evaluated or improved. Examples include environments for software engineering agents and UI-environments for browser-use agents.