Expertise: coding_expert

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 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.

We are seeking highly qualified Domain Experts (SMEs) to support the evaluation, testing, and improvement of advanced Large Language Models (LLMs). You will apply your subject-matter expertise to assess AI-generated responses, identify knowledge gaps, design challenging prompts, and improve model accuracy, reliability, and reasoning. Main tasks listed: Create complex, domain-specific prompts to test LLM capabilities; Evaluate AI-generated responses for factual accuracy, reasoning, relevance, and overall quality; Identify factual errors, logical inconsistencies, knowledge gaps, and model weaknesses; Develop challenging test cases and edge cases that expose model limitations.

We are looking for senior software engineers with 7+ years of industry experience to support the training and evaluation of large language models. In this role, you will review, analyze, and improve AI-generated code, ensuring that it is correct, secure, maintainable, scalable, and aligned with production engineering standards. This is a code quality and engineering evaluation role—not a traditional software testing or manual QA position.

We are seeking Life Sciences Subject Matter Experts to design advanced scientific-reasoning tasks for LifeSciBench. This is a non-coding role focused on biological knowledge, experimental interpretation, data analysis, and rigorous scientific reasoning. You will create challenging, objectively defensible problems based on realistic research scenarios, experimental results, biological data, figures, sequences, structures, and literature-style evidence. 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 looking for a Strategic Project Lead with direct experience operating software-engineering data programs for frontier AI models. You will own production across coding data, RLHF or preference data, SWE benchmarks, model evaluations, agentic coding, code generation, and code repair. This includes contributor calibration, throughput, quality, rework, cost, timelines, and delivery recovery. The role requires strong technical judgment.

Turing builds large-scale datasets and reinforcement learning environments that power post-training for leading AI labs and enterprises. 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. Main tasks listed: Conduct Research on Frontier AI Systems; Investigate the capabilities, limitations, and training methods of frontier AI systems; 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.

We are seeking Life Sciences Subject Matter Experts to design advanced scientific-reasoning tasks for LifeSciBench. This is a non-coding role focused on biological knowledge, experimental interpretation, data analysis, and rigorous scientific reasoning. You will create challenging, objectively defensible problems based on realistic research scenarios, experimental results, biological data, figures, sequences, structures, and literature-style evidence. 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.