Staff Research Scientist at Turing

Job description

About Turing Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com . The Role Turing is seeking exceptional Research Scientists to join our research organization and develop new ways to evaluate, train, and improve frontier AI systems. This is a research-first role focused on problems where the right benchmark, dataset, or methodology often does not yet exist. You will identify important gaps in the literature, propose ambitious new research directions, and take projects from initial hypothesis through experimentation, benchmark construction, and publication. Our research is deliberately focused on frontier evaluation, synthetic data, hallucination and reliability, and agentic science . We are looking for scientists who can recognize important problems early, formulate them precisely, and design rigorous research programs to answer them. What You'll Do Frontier benchmarks and evaluation Identify high-impact gaps in existing benchmark and evaluation literature. Design novel benchmarks in and across STEM fields and on general model functionality. Develop evaluations for emerging model capabilities that are poorly captured by traditional static benchmarks. Design rigorous task-generation, grading, contamination-control, difficulty-calibration, and validation methodologies. Build benchmarks that can become both valuable research contributions and meaningful standards for evaluating frontier models. Synthetic data and post-training Develop methods for generating high-quality synthetic STEM training data. Study how task selection, difficulty, diversity, verification, filtering, and data quality affect downstream performance. Explore methods for generating useful training signal in domains where expert human data is scarce or expensive. Design experiments that determine when synthetic data genuinely improves capabilities rather than simply increasing training volume. Hallucination, reliability, and verification Study hallucination, uncertainty, calibration, and epistemic failure in technical domains. Develop evaluations and methods for improving factual reliability, self-correction, verification, citation, and appropriate abstention. Investigate when models should reason internally, invoke tools, seek external evidence, or recognize that they do not know. Agentic science Research AI systems capable of performing extended scientific and technical work. Develop workflows involving literature search, coding, simulation, tool use, experimentation, verification, and iterative reasoning. Evaluate long-horizon scientific agents and identify the bottlenecks preventing them from reliably performing real research. Explore new approaches to human-AI and multi-agent scientific collaboration. New research directions The areas above are our core focus, not an exhaustive list. Researchers will also have significant latitude to propose new programs in areas such as reasoning, model evaluation, AI-for-science, data generation, and emerging capabilities. What We’re Looking For PhD or equivalent research experience in machine learning, computer science, mathematics, physics, chemistry, biology, engineering, statistics, or another highly technical field . Demonstrated ability to formulate and execute original research. Strong understanding of modern LLMs and the frontier AI research landscape. Excellent experimental design, quantitative reasoning, and scientific judgment. Ability to rapidly understand unfamiliar technical literature and develop expertise in new areas. Strong Python skills and the ability to independently build research prototypes and evaluation pipelines. Excellent technical writing and communication. Comfort working in a fast-moving environment where the research agenda evolves with the frontier. A strong publication record is valuable, but we care most about whether you can identify important questions, design rigorous ways to answer them, and execute quickly enough for the results to matter . 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 Values We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value. We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity. Advantages of joining Turing Work at the frontier of AI , helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks. Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS. Bring frontier AI innovation to the enterprise , applying lessons learned from leading AI labs to solve real-world business challenges. Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies. Move at the pace of AI innovation , with the speed, ownership, and impact of a startup. Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace and celebrate authenticity, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles. For applicants from the European Union, please review Turing's GDPR notice here .

Requirements

Must-haves

Turing is seeking exceptional Research Scientists to join our research organization and develop new ways to evaluate, train, and improve frontier AI systems.
Frontier benchmarks and evaluation
Identify high-impact gaps in existing benchmark and evaluation literature.
Design novel benchmarks in and across STEM fields and on general model functionality.
Develop evaluations for emerging model capabilities that are poorly captured by traditional static benchmarks.
Design rigorous task-generation, grading, contamination-control, difficulty-calibration, and validation methodologies.
Build benchmarks that can become both valuable research contributions and meaningful standards for evaluating frontier models.
Synthetic data and post-training
Develop methods for generating high-quality synthetic STEM training data.
Study how task selection, difficulty, diversity, verification, filtering, and data quality affect downstream performance.
Explore methods for generating useful training signal in domains where expert human data is scarce or expensive.
Design experiments that determine when synthetic data genuinely improves capabilities rather than simply increasing training volume.
Hallucination, reliability, and verification
Study hallucination, uncertainty, calibration, and epistemic failure in technical domains.
Develop evaluations and methods for improving factual reliability, self-correction, verification, citation, and appropriate abstention.
Investigate when models should reason internally, invoke tools, seek external evidence, or recognize that they do not know.
Agentic science
Research AI systems capable of performing extended scientific and technical work.
Develop workflows involving literature search, coding, simulation, tool use, experimentation, verification, and iterative reasoning.
Evaluate long-horizon scientific agents and identify the bottlenecks preventing them from reliably performing real research.
Explore new approaches to human-AI and multi-agent scientific collaboration.
New research directions
What We’re Looking For
PhD or equivalent research experience in machine learning, computer science, mathematics, physics, chemistry, biology, engineering, statistics, or another highly technical field .

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningpythonagile

Platform: greenhouse
Effort: quick