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How much do variant curation scientist jobs pay per hour?

As of Jul 11, 2026, the average hourly pay for variant curation scientist in the United States is $38.99, according to ZipRecruiter salary data. Most workers in this role earn between $33.89 and $42.55 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Variant Curation Scientist position, and why are they important?

To thrive as a Variant Curation Scientist, you need a strong background in genetics, molecular biology, and bioinformatics, typically supported by an advanced degree in a relevant field. Experience with tools such as genome browsers, variant annotation software, and familiarity with databases like ClinVar are highly valued, and certification from organizations like the American Board of Medical Genetics and Genomics (ABMGG) is often preferred. Attention to detail, critical thinking, and strong written communication skills set top candidates apart in this role. These abilities are essential for accurately interpreting genetic data, collaborating with clinical teams, and contributing to high-quality patient care and research.

What does a typical workday look like for a Variant Curation Scientist?

A typical workday for a Variant Curation Scientist involves reviewing and interpreting genetic variants found in DNA sequencing data, updating and maintaining variant databases, and preparing detailed reports for clinical or research use. You may spend time collaborating closely with clinical geneticists, laboratory staff, and bioinformaticians to resolve challenging cases or discuss novel findings. Daily responsibilities also include literature review to stay current with emerging data and occasionally participating in team meetings or case conferences. The role is generally structured within a laboratory or research team, promoting a highly collaborative and knowledge-sharing environment.

What does a Variant Curation Scientist do?

A Variant Curation Scientist analyzes genetic variations to determine their clinical significance, aiding in patient diagnosis and treatment. They review scientific literature, apply bioinformatics tools, and follow established guidelines to classify genetic variants. Their work supports genetic testing laboratories, researchers, and healthcare providers by ensuring accurate interpretation of genetic data. Strong attention to detail, critical thinking, and knowledge of molecular genetics are essential for this role.

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Variant Curation Scientist jobs near you
Research Scientist, Life Sciences

Research Scientist, Life Sciences

Anthropic

San Francisco, CA

Other

Re-posted 8 days ago

Job description

We're seeking an exceptional Research Scientist to join our Life Sciences team at Anthropic. Our team is building a world-class research group focused on making Claude a superhuman life sciences research assistant. This role sits at the intersection of machine learning, software engineering, and biology - you'll directly improve model capabilities on scientific tasks through post-training, evaluation design, and RL environment development.

As a core member of our Life Sciences team, you'll work in a high-impact team that translates deep biological domain knowledge into model training objectives, benchmarks, and agentic workflows. You'll help establish Anthropic as a leader in AI-accelerated biology while shaping how frontier models reason about and execute computational biology tasks.

This role offers a unique opportunity to shape how frontier AI models learn to do biology. You'll work alongside some of the world's best AI researchers while tackling problems that matter for human health and scientific understanding. If you're excited about turning your computational biology expertise into model capabilities, we want to hear from you.

Key Responsibilities

  • Build and ship agentic tools and integrations that let Claude execute real life science workflows - bioinformatics pipelines, database queries, analysis notebooks, literature review

  • Design and build evaluation benchmarks that measure model capabilities on biology tasks - figure interpretation, bioinformatics, protocol reasoning, literature synthesis

  • Work closely with product and design teams to scope, prototype, and ship features for life sciences users

  • Partner with external biotech, pharma, and academic users to understand their workflows and turn feedback into product improvements

  • Build and maintain the engineering infrastructure behind our biology product surface - tool scaffolding, data pipelines, eval harnesses

  • Translate biological domain knowledge into product requirements and evaluation criteria that guide model improvement

Minimum Qualifications

  • Experience applying ML and software engineering to biological problems - computational biology, bioinformatics, protein ML, genomics, or similar

  • Experience working in drug discovery or development at a biotech or pharma company, or conducted fundamental research in an academic setting - with an understanding of what real scientific workflows look like and where they break down

  • Strong software engineering skills: comfortable building production-quality Python, working in large codebases, and owning infrastructure end-to-end

  • Hands-on experience training or fine-tuning ML models (LLMs, protein language models, or other deep learning architectures)

  • A track record of shipping computational tools or pipelines that biologists actually use

  • Comfortable navigating ambiguity and defining problems in a rapidly evolving research environment

  • Able to work independently while collaborating tightly with research, product, and domain-expert teams

  • Results-oriented with a bias toward rapid iteration and measurable impact

  • Passionate about using AI to accelerate scientific discovery while maintaining high ethical standards

Preferred Qualifications
  • 5+ years of experience applying ML and software engineering to biological problems - computational biology, bioinformatics, protein ML, genomics, or similar

  • Ph.D. in computational biology, bioinformatics, bioengineering, CS, or a related quantitative field - or equivalent industry experience

  • Experience with LLM post-training: RLHF, RL from verifiable rewards, SFT data curation, or eval-driven development

  • Direct experience with therapeutic discovery pipelines - target identification, lead optimization, ADMET modeling, or clinical data analysis

  • Familiarity with bioinformatics tooling and pipelines (sequence analysis, structure prediction, single-cell, variant calling, etc.)

  • Experience building agentic systems or tool-use environments

  • Published research in ML for biology, or open-source contributions to computational biology tools

  • Fluency with biological databases (UniProt, PDB, Ensembl, NCBI) and the ability to reason about their schemas and failure modes

Apply