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Variant Scientist Jobs in Oregon (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

Sr. Scientist

OR · On-site +1

Expert knowledge of bioinformatics tools for data processing, including mapping, variant calling ... scientists, biostatisticians, regulatory affairs, and external stakeholders. Experience managing ...

Natera

OR

$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

OR

... performance, variant interpretation, software defects) * Support post-market surveillance ... Partner with engineering and data science teams to evaluate software-related complaints and ...

Netflix

OR

$388K - $619K/yr

In this role, you will partner closely with data scientists and other engineers to build low ... Scala) and SQL (any variant) You strive to write elegant and maintainable code, and you're ...

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Variant Scientist information

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$17

$46

$81

How much do variant scientist jobs pay per hour?

As of Jul 18, 2026, the average hourly pay for variant scientist in Oregon is $46.93, according to ZipRecruiter salary data. Most workers in this role earn between $34.05 and $59.46 per hour, depending on experience, location, and employer.

What Does a Variant Scientist Do?

As a variant scientist, you work for a research laboratory in a university or a medical facility to test and study variations of genes and the effects they have on human development. As part of your duties, you aid in the development of new tests to discover gene abnormalities, perform analysis on patient samples to identify possible mutations, and record your findings to assist in scientific research. You also have heavy reporting responsibilities that may require in-depth computer and writing skills, the ability to analyze data, and strong attention to detail. In this role, you may cater to a specific health field, like pediatrics or oncology.

What are some common challenges faced by Variant Scientists in interpreting genetic data, and how are these typically addressed within a team?

Variant Scientists often encounter challenges such as distinguishing between benign and pathogenic variants, managing large volumes of sequencing data, and staying updated with rapidly evolving genetic databases. These challenges are typically addressed by collaborating closely with bioinformaticians, clinical geneticists, and laboratory personnel to review findings and validate interpretations. Regular team meetings, use of standardized classification guidelines like ACMG, and leveraging advanced software tools help ensure accurate and consistent variant analysis.

What are Variant Scientists?

Variant Scientists are professionals who analyze genetic variants—differences in DNA sequences—to determine their significance in health and disease. They interpret genomic data, often from whole-genome or exome sequencing, to assess whether specific variants may cause or contribute to medical conditions. Their work is crucial in clinical genetics, precision medicine, and biomedical research, helping guide patient diagnosis and treatment. Variant Scientists collaborate with clinicians, bioinformaticians, and laboratory personnel to provide accurate and actionable genetic insights.

What are the key skills and qualifications needed to thrive as a Variant Scientist, and why are they important?

To thrive as a Variant Scientist, you need a solid background in genetics, molecular biology, and bioinformatics, typically supported by an advanced degree such as a PhD or MSc in a related field. Familiarity with next-generation sequencing (NGS) platforms, variant annotation tools, and data analysis software like GATK or ANNOVAR is essential. Strong analytical thinking, attention to detail, and effective communication skills help you interpret complex genetic data and collaborate with multidisciplinary teams. These skills ensure accurate variant interpretation, drive discoveries, and support precision medicine initiatives.
More about Variant Scientist jobs
What are the most commonly searched types of Variant Scientist jobs in Oregon? The most popular types of Variant Scientist jobs in Oregon are:
What are popular job titles related to Variant Scientist jobs in Oregon? For Variant Scientist jobs in Oregon, the most frequently searched job titles are:
What job categories do people searching Variant Scientist jobs in Oregon look for? The top searched job categories for Variant Scientist jobs in Oregon are:
What cities in Oregon are hiring for Variant Scientist jobs? Cities in Oregon with the most Variant Scientist job openings:
Variant Scientist jobs near you
Infographic showing various Variant Scientist job openings in Oregon as of July 2026, with employment types broken down into 1% As Needed, 90% Full Time, 6% Part Time, 2% Contract, and 1% Nights. Highlights an 92% Physical, 2% Hybrid, and 6% Remote job distribution, with an average salary of $97,614 per year, or $46.9 per hour.
Staff Machine Learning Scientist, Agentic AI

Staff Machine Learning Scientist, Agentic AI

Natera

OR • On-site, Remote

Other

Re-posted 13 days ago

Natera rating

7.7

Company rating: 7.7 out of 10

Based on 35 frontline employees who took The Breakroom Quiz

51st of 105 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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