About Turing:
Turing is one of the world’s fastest-growing AI companies, accelerating the advancement and deployment of powerful AI systems.
Turing helps customers in two ways: Working with the world’s leading AI labs to advance frontier model capabilities in thinking, reasoning, coding, agentic behavior, multimodality, multilinguality, STEM, and frontier knowledge; and leveraging that work to build real-world AI systems that solve mission-critical priorities for companies.
Role Overview:
In this role, you will work on cutting-edge initiatives to fine-tune large language models by leveraging your deep analytical skills and mathematics expertise. The ideal candidate possesses a strong foundation in Mathematics —spanning advanced engineering entrance levels through graduate and PhD-level concepts—and the ability to break down complex phenomena into clear, step-by-step reasoning. You will play a direct role in probing model limitations while learning how to leverage frontier AI tools to future-proof your career.
Requirements:
- Core Skills: Strong analytical, research, and problem-solving skills with excellent English comprehension.
- Communication & Feedback: Exceptional structured written communication, with the ability to provide constructive feedback and detailed annotations in a remote environment.
- Analytical & Creative Thinking: Strong lateral thinking capabilities to construct novel scenarios and evaluate complex reasoning pathways.
- Independence: Self-motivated and capable of operating independently in a fast-paced, remote-first setup.
- Technical Infrastructure: Personal desktop/laptop equipped with a stable, high-speed internet connection.
Responsibilities:
In this role, you will help set the benchmark for AI capabilities in advanced physical sciences. Your day-to-day responsibilities will include:
- Problem Creation & Solutioning: Designing and solving challenging physics problems that test the boundaries of large language models.
- Authoring Gold-Standard Data: Creating clear, high-quality, step-by-step solutions with detailed and articulated reasoning.
- Research Collaboration: Working with LLM researchers to align task designs with evaluation goals, targeting key areas where models struggle (e.g., symbolic manipulation, abstraction, multi-step reasoning).
- Benchmark Definition: Helping define and build new evaluation benchmarks across physics curricula ranging from early undergraduate to PhD-level topics.
Education & Experience:
- Educational background or Doctorate in Mathematics, or an equivalent technical field.
- Experience in AI evaluation, data annotation, content review, quality assurance, or a related analytical role is preferred but not required.
Offer Details:
- Commitments Required: at least 4 hours per day and upto 40 hours per week with 4 hours of overlap with PST.
- Engagement type: Contractor
- Engagement Length: 12 weeks
Evaluation Process:
- Shortlisted candidates will be sent a Job Interest Form.
- Final selected candidates will be contacted with the next steps, including onboarding requirements.
