Jobs at Turing

Connects frontier AI model APIs to evaluation harnesses, runs large-scale benchmarks, analyzes model performance and failure modes, and translates findings into client reports. Supports technical pre-sales with AI labs, manages internal data systems and catalogs, curates datasets, audits provenance, and owns precise last-mile data packaging and delivery. The role requires strong Python, API, SQL, model evaluation, data manipulation, and client communication skills.

Leads investor relations, IPO readiness, M&A, strategic investments, and capital allocation. Builds the company’s M&A playbook, ventures program, investor narrative, and related cross-functional processes. Partners with the CFO, CEO, board, product, engineering, and go-to-market teams on transactions, valuation, diligence, negotiations, integration, strategic planning, and build-buy-partner decisions.

Lead end-to-end delivery of software-engineering data programs for frontier AI labs: design and run data pipelines, coordinate 100–1,000+ contributors, ensure annotation and dataset quality, manage client relationships, diagnose bottlenecks, and codify scalable operational playbooks.

Build and operate infrastructure for evaluating coding agents and language models. Design end-to-end features spanning data models, APIs, background workers, and UI; run sandboxed trials; score and aggregate results; improve benchmark quality checks; integrate models and agents; and maintain performance, reliability, concurrency, migrations, tests, and code quality.

Own Turing’s AI ecosystem partner channel, driving partner-sourced revenue, co-selling, referrals, pipeline, and enterprise account planning. Manage partner programs including onboarding, deal registration, certifications, tiers, funding, training, reporting, business reviews, and executive engagement. Coordinate competing technology partners fairly, ensure delivery commitments are staffed, and collaborate across sales, delivery, and industry teams. Build case studies, grow channel revenue, and potentially develop a partner team.

Conduct research on frontier AI systems and develop datasets, experiments, prototypes, tooling, evaluation frameworks, and reinforcement learning environments. Investigate synthetic and agentic data generation, post-training, model understanding, benchmarks, and evaluation. Collaborate with research, engineering, product, and operations teams to translate findings into scalable AI applications, while contributing to technical publications, open-source work, mentorship, and the broader AI research community.

Conduct original research to develop frontier STEM benchmarks, evaluations, synthetic data methods, hallucination and reliability techniques, and agentic science workflows. Responsibilities include identifying research gaps, designing experiments, building evaluation pipelines, validating results, constructing benchmarks, and publishing findings. The role requires strong knowledge of modern LLMs, scientific judgment, quantitative reasoning, Python, technical writing, and independent research execution. This is an in-office position five days per week in San Francisco, Palo Alto, or Seattle.

Conduct frontier AI research and build research-grade datasets, prototypes, tooling, reinforcement learning environments, benchmarks, and evaluation frameworks. Design rigorous experiments, train and evaluate models, investigate synthetic and agentic data generation, and translate findings into scalable products. Collaborate with research, engineering, product, and operations teams, communicate technical insights, contribute to publications and open-source work, and mentor peers. This is a fully onsite role based in San Francisco, Palo Alto, or Seattle.

Conduct original research to evaluate, train, and improve frontier AI systems. Responsibilities include developing STEM benchmarks, synthetic data methods, reliability and hallucination evaluations, verification techniques, and agentic science workflows. The role involves identifying research gaps, designing rigorous experiments, building evaluation pipelines, publishing findings, and proposing new research directions across reasoning, AI evaluation, data generation, and AI-for-science.

Conduct original research to evaluate, train, and improve frontier AI systems. Design benchmarks, synthetic-data methods, reliability and hallucination evaluations, verification techniques, and agentic science workflows. Identify research gaps, formulate hypotheses, build experiments and evaluation pipelines, publish findings, and develop new research directions across STEM and AI. The role requires strong Python skills, quantitative reasoning, technical writing, and expertise with modern LLMs.

Conduct frontier AI research and build research-grade datasets, prototypes, tooling, reinforcement learning environments, benchmarks, and evaluation frameworks. Investigate synthetic and agentic data generation, post-training, model understanding, and model capabilities. Design rigorous experiments, analyze results, and translate findings into scalable products and AI systems while collaborating across research, engineering, product, and operations teams.

Own the end-to-end development and delivery of reinforcement learning environments for frontier AI labs and enterprise clients. Lead environment design, task generation, reward and verifier development, data quality, evaluation, and post-training experiments. Build and manage cross-functional engineering, research, domain expert, QA, and data operations teams. Establish technical processes, schemas, APIs, validation scripts, and review systems while managing customer relationships, solutioning, delivery, and project growth.

Owns end-to-end development of datasets, reinforcement learning environments, and evaluations for coding agents and software engineering models. Leads engineers, researchers, QA professionals, and data creators; establishes quality processes, pipelines, validation, and review workflows. Collaborates directly with frontier AI labs, fine-tunes models, builds benchmarks, develops automation and internal tools, and supports customer-facing enablement. The role combines hands-on technical leadership, engineering management, operational execution, research, and industry thought leadership.

The Staff Research Engineer - Code will lead the creation of datasets and reinforcement learning environments for coding agents, collaborating with AI labs to influence model improvements and operational excellence, while managing a technical team.