Mathematical Systems & Abstract Reasoning Engineer

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 proof of concept into proprietary intelligence with systems that perform reliably, deliver measurable impact, and drive lasting results on the P&L

Role Overview

We are looking for a Biology Expert with experience working with Large Language Models (LLMs) to support the development of AI-driven solutions in the life sciences domain. This role focuses on applying biological knowledge to improve, evaluate, and guide LLM-based systems used for scientific research, healthcare, and biotech applications.

Key Responsibilities :

• Provide domain expertise in biology to guide LLM-based applications and use cases.
• Evaluate and validate LLM-generated outputs for scientific accuracy and relevance.
• Design and refine prompts and workflows for biology-related tasks such as:literature summarization
  biological question answering
  data annotation and labeling
  knowledge extraction from research papers
  
• Review and curate biological datasets used for training or testing AI models.
• Collaborate with AI/ML teams to improve model performance in life sciences contexts.
• Identify gaps, biases, or inaccuracies in AI-generated biological content.
• Assist in building domain-specific benchmarks and evaluation frameworks.
• Stay updated on advancements in biology and AI applications in life sciences.

Required Qualification

• Master’s or PhD in Biology, Biotechnology, Genetics, Molecular Biology, Bioinformatics, or related field.
• Strong understanding of biological concepts and scientific literature.
• Experience using or working with LLMs, NLP tools, or AI-based platforms.
• Ability to critically evaluate scientific content for accuracy and consistency.
• Familiarity with biological databases and research resources (e.g., PubMed, NCBI, UniProt).
• Strong analytical thinking and attention to detail.
• Good communication skills for cross-functional collaboration

Offer Details:

• Commitments Required: at least 4 hours per day and minimum 40 hours per week with 4 hours of overlap with PST.
• Engagement type: Contractor
• Engagement Length: 5 weeks

Evaluation Process -

• Shortlisted candidates will be sent a Job Interest Form.
• Shortlisted candidates will be contacted to discuss the pre‑onboarding requirements