Company: Turing

We are hiring US-based academic dermatologists to contribute senior clinical expertise to data curation for frontier AI models and labs. The work supports a longitudinal dermatology dataset used by AI developers, regulatory teams, and academic groups. This is the senior reviewer track of the project. You will lead clinical review on complex cases, contribute to the development of clinical evaluation frameworks, and serve as expert advisor for client teams reviewing AI model outputs. Main tasks listed: Lead senior-level clinical review on complex or ambiguous cases; Facilitate structured resolution of clinical disagreement among reviewers; Contribute to the development of clinical evaluation frameworks; Provide expert advisory hours to client teams reviewing AI model outputs. The listing says the work can involve sensitive or graphic content.

This role is part of a project supporting leading LLM companies. The primary objective is to help these foundational LLM companies improve their Large Language Models. We support companies in enhancing their models by offering high-quality proprietary data. This data can be used as a basis for fine-tuning models or as an evaluation set to benchmark the performance. Main tasks listed: Translate CUDA/C++ code into equivalent Python implementations using PyTorch and NumPy, ensuring logical and performance parity; Analyze CUDA kernels and GPU-accelerated code for structure, efficiency, and function before translation; Evaluate LLM-generated translations of CUDA/C++ code to Python, providing technical feedback and corrections; Collaborate with prompt engineers and researchers to develop test prompts that reflect real-world CUDA/PyTorch tasks.

Evaluate LLM models for areas of finance where models do not perform well. Create rubrics to assess model capabilities on specific areas of your finance expertise (such as deal analysis, M&A assessments, and more). Main tasks listed: Evaluate LLM models for areas of finance where models do not perform well; Create rubrics to assess model capabilities on specific areas of your finance expertise (such as deal analysis, M&A assessments, and more); Collaborate with AI researchers and fellow finance experts to shape training methods, evaluation strategies, and benchmarks.

We are seeking an experienced Civil or Structural Engineering expert to lead a pod of trainers building realistic, terminal-based technical tasks for Terminal Bench Science. You will review every task your trainers produce for engineering correctness, reproducibility, and grader reliability, and coach trainers to consistently deliver high-quality tasks. Main tasks listed: Review engineering tasks end to end, including problem statements, structural and geotechnical models, load cases, simulation data, computational environments, reference solutions, and automated tests; Validate tasks involving structural analysis, finite-element methods (FEA), earthquake and wind engineering, structural dynamics, geotechnical modeling, hydraulics, hydrology, transportation, and infrastructure systems; Ensure tasks require genuine multi-step engineering reasoning and reflect realistic civil and structural engineering workflows; Verify engineering accuracy across units, equilibrium, boundary conditions, load combinations, design codes, numerical stability, convergence, and safety factors.

We are looking for Software Engineers to work with a leading Frontier LLM Lab to help accelerate their Agentic Data Cloud roadmap by building and scaling tools for next-generation AI-powered data systems. You will work on developing MCP tools, improving AI-assisted developer workflows, and expanding platform capabilities across the Data Cloud ecosystem. Main tasks listed: Build and enhance MCP tools for both proprietary and open-source environments; Expand test coverage for tools used by AI coding assistants (e.g., Gemini CLI, Claude Code); Refactor, maintain, and improve existing test frameworks and codebases; Convert technical blogs into production-ready sample code and documentation.

We are looking for experienced Software Engineers with a strong background in developing and operating large-scale production systems. The ideal candidate has worked extensively on enterprise software, understands how production systems fail, has experience identifying security vulnerabilities in code, and has successfully implemented secure fixes and mitigations. Main tasks listed: Design and build realistic coding-agent benchmark tasks using production-like repositories, tests, configs, documentation, and runtime scenarios; Create benign engineering tasks such as bug fixes, feature additions, CI repairs, config migrations, integration updates, and runtime-state fixes; Define clear utility requirements to verify that the coding agent completes the requested task correctly; Define safety/alignment requirements to ensure the agent preserves system constraints, developer intent, data integrity, privacy, permissions, and oversight mechanisms.

We are seeking Life Sciences Subject Matter Experts to develop advanced scientific reasoning tasks for LifeSciBench. Unlike coding-focused benchmarks, this role emphasizes deep biological knowledge, scientific interpretation, and rigorous reasoning. You will create challenging questions based on realistic scientific scenarios, experimental results, biological data, figures, literature-style evidence, and research problems. This is a non-code benchmark: tasks are not executed, and no programming is required. Main tasks listed: Design novel, graduate-level life sciences problems grounded in realistic research contexts; Develop clear questions and rigorous reference answers with defensible scientific reasoning; Interpret experiments, datasets, figures, sequences, structures, and other biological evidence; Validate biological claims, calculations, assumptions, and conclusions.

We are seeking experienced Earth Sciences experts to develop realistic, terminal-based scientific and computational tasks for a frontier AI training and evaluation initiative. In this role, you will design challenging scientific tasks involving climate science, atmospheric processes, geophysics, oceanography, geology, hydrology, remote sensing, and environmental modeling. Main tasks listed: Design realistic, multi-step scientific tasks based on authentic Earth Sciences, climate, geospatial, environmental, and geophysical research workflows; Prepare and process scientific datasets involving climate, weather, atmospheric conditions, oceanography, geology, hydrology, satellite imagery, and environmental systems; Develop computational tasks involving geospatial analysis, time-series processing, numerical modeling, interpolation, forecasting, remote sensing, and environmental risk analysis; Build reproducible, terminal-based computational environments using scientific libraries, command-line tools, and appropriate dependencies. The listing says the work can involve sensitive or graphic content.

We are seeking an experienced Chemical Engineering expert to lead a team of technical trainers developing realistic, terminal-based engineering tasks for a frontier AI training and evaluation initiative. In this role, you will oversee a pod of approximately 5–10 trainers, review chemical engineering tasks for technical accuracy, reproducibility, and evaluation quality, and ensure tasks reflect authentic chemical engineering workflows. Main tasks listed: Lead and mentor a pod of technical trainers, ensuring consistent quality, productivity, and timely delivery of chemical engineering tasks; Review engineering tasks end to end, including problem statements, reaction kinetics, thermodynamic and property data, process flowsheets, computational environments, reference solutions, and automated tests; Validate tasks involving reaction engineering, thermodynamics, phase equilibria, transport phenomena, separation processes, process simulation, process control, optimization, combustion, and electrochemical engineering; Ensure tasks reflect realistic chemical engineering workflows and require meaningful multi-step technical reasoning.

About Turing:Based in San Francisco, California, Turing is the world’s leading research accelerator for frontier AI labs and a trusted partner for global enterprises deploying advanced AI systems. Turing supports customers in two ways: first, by accelerating frontier research with high-quality data, advanced training pipelines, plus top AI researchers who specialize in coding, reasoning, STEM, multilinguality, multimodality, and agents; and second, by applying that expertise to help enterprises transform AI from…