Turing: Staff Research Engineer – Frontier AI, RL & Evaluation

Job Overview:
We are seeking a highly skilled and innovative Staff Research Engineer specializing in Frontier AI, Reinforcement Learning (RL), and Evaluation. In this role, you will lead cutting-edge research efforts to develop large-scale AI models, contribute to advancing the state of the art in machine learning, and collaborate with cross-functional teams to translate research insights into impactful applications.

Our environments evaluate and improve AI models on complex, long-range, multi-step workflows across high-value domains such as Finance, Sales, Retail, Developer Tools, Collaboration, and Customer Experience.

Our work includes:

• Software engineering environments for coding agents
• UI environments for computer-use and browser-use agents
• MCP-based environments for function-calling agents across enterprise and consumer applications

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

This role sits at the intersection of research and engineering. You will investigate high-impact questions, design rigorous experiments, build research-grade prototypes and tooling, and collaborate with Research, Engineering, Product, and Operations teams to translate promising ideas into scalable applications.

You will contribute to areas including synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation.

Rate: $40/hr

Employment Type: Short Term Contract 1099

Location: WFH in ONLY these states

Alabama

Arizona

Colorado

Florida

Idaho

Iowa

Kentucky

Michigan

Minnesota

Mississippi

Missouri

Montana

New York

North Carolina

North Dakota

Oklahoma

Pennsylvania

South Carolina

South Dakota

Virginia

Wisconsin

Wyoming

Shift: 40 hours a week with a 4hr overlap with Pacific Time business hours

Responsibilities:

Conduct Research on Frontier AI Systems

• Investigate the capabilities, limitations, and training methods of frontier AI systems.

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 experimental results and draw clear, evidence-based conclusions.
• Establish strong practices for experimental rigor, data quality, reproducibility, and interpretation.
• Iterate quickly from initial hypotheses to validated technical insights.

Translate Research into Practical Impact

• Collaborate with Research, Engineering, Product, and Operations teams.
• Translate research findings into improvements for Turing’s products, platforms, and AI capabilities.
• 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.

Contribute to the Research Community

• Share findings through technical reports, publications, open-source work, workshops, or conferences 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.
• 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.
• Stay current with advances in machine learning and identify opportunities for meaningful technical contributions.

Qualifications:

• 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.
• Minimum 7+ years of professional experience with significant research engineering experience working on machine learning or frontier AI systems.
• Strong foundations in machine learning and practical experience designing experiments, training models, evaluating models, or developing AI systems.
• Demonstrated research experience in one or more of the following areas:
• Synthetic or agentic data generation
• Reinforcement learning or post-training
• Model understanding
• AI evaluation
• Benchmarks
• AI agents or tool-using systems
• Strong programming skills,…