Company: Turing

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.

The Text Situated Alignment Generalist – BA role supports a short-term human-AI interaction evaluation pilot designed to assess whether providing social context before a user’s request helps a language model generate more relevant, personalized, and user-aligned advice. Contributors create realistic, ongoing social scenarios, interact with the model under different conditions, and evaluate the quality of its responses. Main tasks listed: Create realistic and ongoing social scenarios involving personal, professional, or interpersonal context; Interact with the language model under assigned evaluation conditions; Evaluate model responses for relevance, usefulness, personalization, clarity, and alignment with the provided context; Explain the reasoning behind each rating through clear and detailed written feedback.

The Text Situated Alignment Generalist – BA role supports a short-term human-AI interaction evaluation pilot designed to assess whether providing social context before a user’s request helps a language model generate more relevant, personalized, and user-aligned advice. Contributors create realistic, ongoing social scenarios, interact with the model under different conditions, and evaluate the quality of its responses. Main tasks listed: Create realistic and ongoing social scenarios involving personal, professional, or interpersonal context; Interact with the language model under assigned evaluation conditions; Evaluate model responses for relevance, usefulness, personalization, clarity, and alignment with the provided context; Explain the reasoning behind each rating through clear and detailed written feedback.

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.

You will be the sole Product Manager embedded in a cross-functional engineering team building a cloud-agnostic, multi-tenant platform for deploying, certifying, and distributing AI agents at enterprise scale. This is a greenfield platform engineering programme running across three phases over approximately 12 months, delivered for a government-aligned client. Backlog & Prioritisation Define and maintain the product backlog across all three phases.

We are seeking experienced Certified Health & Wellness Coach Professionals to help evaluate and enhance large language model (LLM) outputs related to health coaching, behavior change, and wellness. You’ll assess AI-generated coaching conversations for accuracy, safety, empathy, and effectiveness — contributing to the development of responsible AI that supports evidence-based health coaching. Main tasks listed: Review AI-generated or human coaching dialogue for accuracy, tone, empathy, and actionability; Annotate quality dimensions such as goal-setting effectiveness, motivational technique, and personalization; Identify unsafe, misleading, or non-evidence-based advice and flag for correction; Compare responses against best-practice coaching frameworks (e.g., motivational interviewing, behavior change models).

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.