Senior Research Scientist, STEM

turing; United States; Remote; USD 250,000 - 350,000; Full time

Benefits offered by this job

Equity

Publication opportunities

Job summary

Turing is seeking a researcher to advance frontier AI evaluation, training, and improvement. You will tackle problems where benchmarks and datasets are not yet defined, proposing new directions from scratch and publishing in leading venues.

The role emphasizes designing novel benchmarks across STEM domains, generating high-quality synthetic data, and evaluating model reliability, uncertainty, and abstention, with strong autonomy and significant scientific impact.

Qualifications

• PhD or equivalent research experience in a highly technical field (ML, CS, math, physics, chemistry, biology, engineering, or statistics).
• Ability to formulate and execute original research projects.
• Strong grounding in modern large language models and frontier AI research landscape.
• Excellent experimental design and quantitative analysis skills.

Responsibilities

• Identify gaps in benchmark/evaluation literature and design novel benchmarks for STEM domains.
• Develop rigorous task-generation, grading, contamination-control, and validation methodologies to set new evaluation standards.
• Create methods for high-quality synthetic STEM training data and run experiments to separate capability gains from training volume.
• Investigate hallucination, uncertainty, calibration, and epistemic failure; build evaluations for reliability, self‑correction, verification, and abstention.
• Research long-horizon scientific agents and design workflows combining literature search, coding, simulation, tool use, experimentation, and iterative reasoning.
• Pursue new directions in reasoning, model evaluation, AI-for-science, data generation, and emerging capabilities.

Skills

Original research

Quantitative analysis

Experimental design

LLM fundamentals

Education

PhD or equivalent

Job description

Role overview

This position focuses on STEM‑focused research that advances how frontier AI systems are evaluated, trained, and improved. The work targets problems where the right benchmark, dataset, or methodology does not yet exist, and where researchers must propose new directions from scratch. Projects span hypothesis generation, experimentation, benchmark construction, and publication across frontier STEM evaluation, synthetic data, hallucination and reliability, and agentic science.

Responsibilities

• Identify important gaps in benchmark and evaluation literature and design novel benchmarks within and across STEM domains.
• Develop rigorous task‑generation, grading, contamination‑control, difficulty‑calibration, and validation methodologies that set new standards for evaluating frontier models.
• Create methods for generating high‑quality synthetic STEM training data and run experiments that separate genuine capability gains from raw training volume.
• Investigate hallucination, uncertainty, calibration, and epistemic failure, and build evaluations for factual reliability, self‑correction, verification, citation, and appropriate abstention.
• Research long‑horizon scientific agents and design workflows combining literature search, coding, simulation, tool use, experimentation, and iterative reasoning.
• Pursue new research directions in reasoning, model evaluation, AI‑for‑science, data generation, and emerging capabilities.

Requirements

• PhD or equivalent research experience in a highly technical field such as machine learning, computer science, mathematics, physics, chemistry, biology, engineering, or statistics.
• Demonstrated ability to formulate and execute original research projects.
• Strong grounding in modern large language models and the frontier AI research landscape.
• Excellent experimental design and quantitative analysis skills.

Benefits and work setup

• Compensation range of $250,000 to $350,000 OTE plus equity.
• Opportunity to publish at leading conferences such as ICLR, ICML, and NeurIPS.
• High autonomy, rapid iteration, and meaningful impact on frontier AI research.