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Clinical Genomic Variant Scientist Jobs (NOW HIRING)

Natera

Staff Machine Learning Scientist, Agentic AI

OR · On-site +1

... including genomic variant calling, LLM fine-tuning, and clinical trial matching pipelines ... PhD or Master's degree in Computer Science, Bioinformatics, Statistics, or a related quantitative ...

Natera

OR · On-site

$126K - $166K/yr

... variant interpretation, clinical review, and reporting. As Natera scales across Women's Health ... Bachelor's degree in life sciences, engineering, computer science, statistics, or equivalent ...

Natera

Senior Product Manager, Clinical Genomics

$129K - $170K/yr

... variant interpretation, clinical review, and reporting. As Natera scales across Women's Health ... Bachelor's degree in life sciences, engineering, computer science, statistics, or equivalent ...

Cook Children's

Bioinformatician

Fort Worth, TX · On-site

This position will collaborate closely with Cook Children's variant scientists, genetic counselors, clinicians, data scientists, and computational teams. Experience * 2 years experience in genomic ...

Variant Bio

(Associate) Scientist, Health Data

Seattle, WA · On-site

$67K - $67K/yr

ABOUT THE ROLE The Health Data team at Variant Bio turns data generated in the field, across our ... Genomic study-design literacy: Able to recognize when recruitment, sampling, batching, or ...

Bionano

Clinical Lab Director

San Diego, CA · On-site

... Genomics, seeks a qualified Clinical Laboratory Director to provide clinical, scientific ... Working knowledge of ACMG/AMP and related professional guidance for copy number, sequence variant ...

BioNano

Clinical Lab Director

San Diego, CA · On-site

... Genomics, seeks a qualified Clinical Laboratory Director to provide clinical, scientific ... Working knowledge of ACMG/AMP and related professional guidance for copy number, sequence variant ...

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All JobsClinical Genomic Variant Scientist Jobs

Clinical Genomic Variant Scientist information

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$28

$54

$81

How much do clinical genomic variant scientist jobs pay per hour?

As of Aug 8, 2026, the average hourly pay for clinical genomic variant scientist in the United States is $54.75, according to ZipRecruiter salary data. Most workers in this role earn between $40.14 and $68.27 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a clinical genomic variant scientist?

To thrive as a Clinical Genomic Variant Scientist, you need expertise in genetics, molecular biology, and bioinformatics, typically backed by an advanced degree in a life science field. Familiarity with next-generation sequencing (NGS) platforms, variant interpretation tools (like ClinVar or HGMD), and clinical reporting software is essential. Strong analytical thinking, attention to detail, and effective communication skills set top performers apart in this role. These competencies are crucial for accurately interpreting genetic data, ensuring reliable clinical reporting, and collaborating effectively with healthcare teams.

What is the difference between Clinical Genomic Variant Scientist vs Molecular Geneticist?

AspectClinical Genomic Variant ScientistMolecular Geneticist
CredentialsMaster's or PhD in genetics, genomics, or related field; certification may be preferredMaster's or PhD in genetics, molecular biology, or related field; certification optional
Work EnvironmentLaboratories, hospitals, research institutions focusing on clinical diagnosticsResearch labs, hospitals, or academic settings focusing on genetic research and testing
Employer & Industry UsageHealthcare providers, diagnostic labs, biotech companiesAcademic institutions, research organizations, clinical labs

The main difference is that Clinical Genomic Variant Scientists focus on interpreting genetic variants for clinical diagnostics, often working directly with patient data, while Molecular Geneticists may work more broadly in research or laboratory settings without direct clinical responsibilities.

How much do clinical genomic variant scientists make?

Clinical genomic variant scientists typically earn between $70,000 and $120,000 annually, depending on experience, education, and location. Salaries can increase with specialized skills, certifications, and working in high-demand healthcare or research environments.

What are some common challenges clinical genomic variant scientists face when interpreting genetic data?

Clinical Genomic Variant Scientists often encounter challenges such as distinguishing between variants of uncertain significance and those with clear clinical relevance, due to the complexity and volume of genomic data. Staying up-to-date with rapidly evolving research and variant databases is crucial, as new discoveries can change the interpretation of previously classified variants. Collaboration with clinicians, genetic counselors, and bioinformaticians is essential to ensure accurate reporting and patient care. Additionally, maintaining rigorous documentation and adhering to clinical guidelines is key to ensuring high-quality results.

What is a clinical genomic variant scientist?

A Clinical Genomic Variant Scientist is a specialist who analyzes and interprets genetic variants found in patient DNA to determine their clinical significance. They work closely with clinicians and genetic counselors to provide insights into how specific genetic changes may contribute to disease, influence treatment, or affect patient outcomes. Their role often involves reviewing genetic data, consulting scientific literature, and applying guidelines to classify variants as benign, pathogenic, or of uncertain significance. This work is crucial for precision medicine and informing patient care. Many Clinical Genomic Variant Scientists are employed in hospitals, diagnostic laboratories, or research institutions.
More about Clinical Genomic Variant Scientist jobs
What job categories do people searching Clinical Genomic Variant Scientist jobs look for? The top searched job categories for Clinical Genomic Variant Scientist jobs are:
Clinical Genomic Variant Scientist jobs near you
Infographic showing various Clinical Genomic Variant Scientist job openings in the United States as of August 2026, with employment types broken down into 90% Full Time, 5% Part Time, and 5% Contract. Highlights an 75% In-person, 5% Hybrid, and 20% Remote job distribution, with an average salary of $113,877 per year, or $54.7 per hour.

Staff Machine Learning Scientist, Agentic AI

Natera

OR • On-site, Remote

Other

Re-posted 3 days ago

Natera rating

7.6

Company rating: 7.6 out of 10

Based on 37 frontline employees who took The Breakroom Quiz

60th of 120 rated laboratories

Job description

POSITION SUMMARY:

Natera is seeking a Staff Machine Learning Scientist - Agentic AI to join our AI team, an advanced R&D and core AI innovation team bridging the gap between molecular discovery and clinical execution. Leveraging a proprietary data moat of over 250,000 oncology patients profiled with longitudinal ctDNA, WES/WGS, digital pathology, and EMR data, you will design and deploy production-grade autonomous AI agents and multi-modal foundation models. Your mission is to architect systems capable of multi-step biological reasoning, converting complex multi-omic datasets into verifiable clinical insights that accelerate biomarker and therapeutic discovery. You will lead the next evolution of our Agentic AI platform, designing autonomous systems capable of reasoning through the complexities of cancer biology, orchestrating proprietary foundation models, and simulating virtual patient trajectories.

PRIMARY RESPONSIBILITIES:

  • Lead the technical design and deployment of multi-agent systems capable of autonomous hypothesis generation and tool use, including genomic variant calling, LLM fine-tuning, and clinical trial matching pipelines
  • Incorporate and advance Natera's transformer-based foundation model by integrating DNA, RNA, and H&E imaging modalities for multi-step biological reasoning and tool use
  • Implement advanced LLM reasoning frameworks, such as ReAct and Chain-of-Thought, alongside reinforcement fine-tuning (RFT) to ensure agents provide accurate, explainable clinical rationales
  • Architect systems that autonomously translate complex, multi-modal data into diagnostic and therapeutic insights with human-verifiable reasoning and tracing
  • Own the technical strategy and product roadmap for agentic workflows across the Biopharma Solutions and Therapeutics Discovery division, converting complex clinical challenges into scalable AI systems
  • Establish production-grade machine learning engineering standards and reproducible architectures across the AI team to ensure absolute model transparency and scientific auditability
  • Drive cross-functional alignment and technical consensus by defending agentic architectures and biological reasoning frameworks in rigorous peer reviews

QUALIFICATIONS:

  • PhD or Master's degree in Computer Science, Bioinformatics, Statistics, or a related quantitative field
  • 8 or more years of experience in AI research or engineering, with a proven track record of moving multi-agent orchestration architectures or large-scale language model workflows from prototype to production
  • Deep experience with agentic frameworks, such as LangChain or Claude Agent SDK, retrieval-augmented generation (RAG), and validation frameworks for autonomous AI agents
  • Strong understanding of cancer genomics (WES/WTS), mutational signatures, and structure-activity relationships
  • Advanced production-level development experience using PyTorch and experience with distributed training on large GPU clusters, including NVIDIA H100s

KNOWLEDGE, SKILLS, AND ABILITIES:

  • Ability to operate with absolute ownership to close operational gaps and independently drive architectural deployment
  • Data-driven decision-making focused on empirical model performance and clinical validity
  • Technical leadership capability to define long-term AI engineering roadmaps
  • Rigor in code architecture, reproducibility, and production-grade software engineering practices
  • Comfort with high intellectual friction and the ability to defend scientific and engineering choices under rigorous internal peer review
  • Focus on translating machine learning outcomes directly into patient-centric clinical utility

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