The Role

Turing builds large-scale datasets and reinforcement learning (RL) environments that power post-training for the world’s leading AI labs and enterprises. We create RL environments to evaluate and improve our customers' models on complex, long-range, multi-step workflows across high-GDP-value domains such as Finance, Sales, Retail, Developer Tools, Collaboration, Customer Experience. 

The environments vary depending on the model capability being evaluated / improved, a few examples of environment types are listed here: 

1. Environments for Software Engineering / coding agents 
2. UI-Environments for Computer-Use/Browser-Use agents 
3. MCP-based Environments for general function-calling agents across various enterprise and consumer applications

We are seeking Research Engineers to advance our understanding of frontier AI systems and develop practical innovations in data, algorithms, evaluation, and training.

You will work at the intersection of research and engineering, investigating high-impact questions that improve real-world AI systems. You will design and run rigorous experiments, develop research-grade prototypes and tooling, and collaborate with Research, Engineering, Product, and Operations teams to translate promising ideas into scalable applications.

This role offers the opportunity to contribute to both foundational research and applied AI development across synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation.

What You’ll Do

1. Conduct Research on Frontier AI Systems

• 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.
• Stay current with advances in machine learning and identify opportunities for meaningful technical contribution.

2. Build and Evaluate Research Systems

• 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 and draw clear, evidence-based conclusions.
• Establish sound practices for experimental rigor, data quality, reproducibility, and interpretation.
• Iterate quickly from early hypothesis through validated technical insight.

3. Translate Research into Practical Impact

• Collaborate closely with Research, Engineering, Product, and Operations teams.
• Translate research findings into improvements for Turing’s products, platforms, and AI capabilities.
• Help identify which ideas are ready to move from exploration into scalable, real-world applications.
• Communicate technical findings clearly to both specialized and cross-functional audiences.

4. Contribute to the Research Community

• Share findings through technical reports, publications, open-source work, workshops, or conference participation where appropriate.
• Contribute to Turing’s research culture through technical discussions, peer review, mentorship, and collaboration.
• Represent Turing thoughtfully within the broader AI research community.

What We’re Looking For

• Research background: PhD or Master’s degree in artificial intelligence, machine learning, computer science, or a closely related technical field; exceptional equivalent research experience will also be considered.
• Technical depth: Strong foundation in machine learning and practical experience designing experiments, training or evaluating models, and working with modern AI tooling.
• Relevant expertise: Demonstrated research experience in synthetic or agentic data generation, reinforcement learning or post-training, AI understanding, evaluation, or benchmarks.
• Research engineering ability: Strong programming skills and the ability to implement, test, and iterate quickly in a research environment.
• Scientific judgment: Sound judgment around experimental rigor, data quality, reproducibility, and evidence-based decision-making.
• Communication and collaboration: Clear written and verbal communication skills, intellectual curiosity, and the ability to work effectively across research and engineering teams.

A note from our CTO, Ece Kamar

Some of the most important questions in AI can't be answered from inside a single lab: how to evaluate frontier models, how data and RL truly drive capability, and what happens when AI meets the real world. At Turing, we work directly with frontier labs, the academic community, and enterprises deploying AI at scale. That creates a feedback loop where deployment shapes our research and our research shapes the field, and we publish our findings, datasets, and benchmarks openly. Join our talented team at Turing to push the frontier of AI with rigorous science that matters.

Why Turing

• Work directly with leading AI labs and enterprises at the frontier of post-training and RL environment design.
• Build datasets and environments that directly improve the capabilities of advanced AI systems.
• Help advance coding agents’ ability to understand, plan, and execute complex software-engineering tasks.
• Apply frontier AI innovations to high-value enterprise workflows.
• Operate with high autonomy, rapid iteration, and meaningful commercial impact.
• Collaborate with exceptional colleagues from organizations including Google, Meta, Amazon, and other leading technology companies.
• Contribute to research that may be shared through technical publications and leading conferences such as ICLR, ICML, and NeurIPS.

Compensation: $250,000 to $400,000 OTE + Equity