Staff Research Engineer, Frontier Data
About this role
The day-to-day work for a Staff Research Engineer, Frontier Data, involves investigating the capabilities and limitations of frontier AI systems to inform Turing's product strategy. This role focuses heavily on advancing the understanding of complex AI systems by exploring new approaches in areas such as synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation. Engineers will design and execute rigorous experiments, develop research-grade prototypes and tooling, and analyze results to derive evidence-based conclusions. A key function is translating these complex research findings into tangible improvements for Turing’s platforms and AI capabilities, ensuring promising ideas move toward scalable, real-world applications.
This position sits at the intersection of deep research and practical engineering. The engineer will collaborate extensively with Research, Engineering, Product, and Operations teams to bridge the gap between foundational research and deployed systems. They will be responsible for building and evaluating the systems that power post-training for leading AI labs and enterprises, focusing on complex, multi-step workflows in high-value domains.
This role is ideal for someone who thrives on tackling high-impact, open-ended technical questions within the AI landscape. A successful candidate will possess the ability to move fluidly between theoretical investigation and hands-on implementation, communicating complex technical insights effectively to diverse audiences.
What you will do
- Investigate the capabilities, limitations, and training methods of frontier AI systems.
- Formulate research questions that can 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, tooling, and evaluation frameworks.
- Train, test, and evaluate models using modern AI and machine learning tools.
- Analyze results carefully to draw clear, evidence-based conclusions.
- Collaborate with cross-functional teams to translate research findings into product improvements.
- Communicate technical findings clearly to specialized and cross-functional audiences.
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 have 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 required for implementing, testing, and iterating quickly within a research environment.
- The role demands sound scientific judgment for interpreting complex experimental results.
- Experience in research engineering is necessary to build and iterate rapidly from hypothesis to insight.
Nice to have
- Experience with UI-Environments for Computer-Use/Browser-Use agents
- Experience with MCP-based Environments for general function-calling agents
Benefits
- $250,000 to $400,000 salary
Skills and tech stack
Questions candidates ask
What is the remote policy for this role?
The posting does not specify the remote work policy for this position.
What is the compensation range for this role?
The salary range for this position is listed between $250,000 and $400,000.
What is the primary tech stack involved?
The role involves working with modern AI and machine learning tools. Specific frameworks are not detailed in the posting.
How is the team structured?
The role requires close collaboration with Research, Engineering, Product, and Operations teams.