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Full Time Bioinformatics Machine Learning Jobs in Happy Valley, OR

Hitachi

Data Scientist I or II (MAD-BS-OR)

Hillsboro, OR · On-site +1

$121K - $167K/yr

Data Analytics/Business Intelligence Job Schedule: Full time Remote: No POSITION: Data Scientist I ... PRIMARY RESPONSIBILITIES * Hands-on development and write algorithms in machine learning ...

Novolex

Machine Operator

Vancouver, WA

$24.90 - $25.90/hr

... Learning Management System that supports and enhances employee skills at all levels of the ... Formerly Pactiv Evergreen Employment Type: FULL_TIME

Novolex

Machine Operator

Vancouver, WA

$24.90 - $25.90/hr

... Learning Management System that supports and enhances employee skills at all levels of the ... Formerly Pactiv Evergreen Employment Type: FULL_TIME

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All JobsBioinformatics Machine Learning JobsFull Time Bioinformatics Machine Learning JobsFull Time Bioinformatics Machine Learning Jobs in Happy Valley, OR

Full Time Bioinformatics Machine Learning information

See Happy Valley, OR salary details

$61.9K

$98.3K

$155.5K

How much do full time bioinformatics machine learning jobs pay per year?

As of Aug 1, 2026, the average yearly pay for full time bioinformatics machine learning in Happy Valley, OR is $98,282.00, according to ZipRecruiter salary data. Most workers in this role earn between $70,200.00 and $134,700.00 per year, depending on experience, location, and employer.

What is the difference between Full Time Bioinformatics Machine Learning vs Full Time Data Scientist?

AspectFull Time Bioinformatics Machine LearningFull Time Data Scientist
Required CredentialsDegree in Bioinformatics, Computer Science, or related field; knowledge of biology and machine learningDegree in Data Science, Statistics, Computer Science, or related field; strong programming skills
Work EnvironmentResearch labs, biotech companies, healthcare institutionsTech companies, finance, consulting firms, various industries
Industry UsageBiotech, healthcare, genomicsFinance, marketing, tech, healthcare
Common Search/ComparisonYesYes

Full Time Bioinformatics Machine Learning roles focus on applying machine learning techniques to biological data, often within healthcare or biotech settings. Full Time Data Scientist positions are broader, covering various industries and data types. While both require programming and statistical skills, bioinformatics roles emphasize biological knowledge, whereas data science roles focus on diverse data applications across industries.

What job categories do people searching Full Time Bioinformatics Machine Learning jobs in Happy Valley, OR look for? The top searched job categories for Full Time Bioinformatics Machine Learning jobs in Happy Valley, OR are:
Full Time Bioinformatics Machine Learning jobs near you
Infographic showing various Full Time Bioinformatics Machine Learning job openings in Happy Valley, OR as of July 2026, with employment types broken down into 6% Locum Tenens, 51% As Needed, 24% Full Time, 5% Part Time, 1% Temporary, and 13% Nights. Highlights an 79% Physical, 3% Hybrid, and 18% Remote job distribution, with an average salary of $98,282 per year, or $47.3 per hour.

Bioinformatics Scientist - Gene Regulation & Cellular Reprogramming

e184

Portland, OR • On-site

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 6 days ago

Job description

About us
e184 Repro is a biotechnology research company with the mission of advancing in vitro gametogenesis to solve one of biology's most profound challenges: returning the fundamental right to procreate.
We work at the frontier of cutting-edge technology, integrating cellular reprogramming, machine learning-guided optimization, multi-omics analysis, and automated experimental workflows to enable gamete development for individuals facing reproductive challenges.
Role overview
As a Bioinformatics Scientist with a cellular reprogramming background, you will lead computational analysis of multi-modal genomics data (scRNA-seq, ATAC-seq) to identify transcription factor combinations driving desired cell state conversion. This role focuses on gene regulatory network inference, differential analysis of single-cell transcriptomics, and computational prioritization of TF cocktails for cellular reprogramming, requiring deep expertise in multi-platform scRNA-seq analysis and transcriptional regulation biology. You will collaborate closely with wet lab teams to translate computational predictions into experimental designs, while also exploring hybrid approaches that integrate foundation model insights into our reprogramming pipeline.
What you'll do
  • Lead end-to-end TF discovery for cellular reprogramming - from multi-platform single-cell genomics analysis (scRNA-seq, ATAC-seq) through GRN inference, differential analysis, and trajectory mapping - to nominate the regulators that flip cell fate.
  • Crack the combinatorial code of reprogramming by ranking TF cocktails as actionable combinations and decoding pooled perturbation and CRISPRa screens at single-cell resolution.
  • Read regulatory grammar straight off the chromatin - accessibility, motifs, synergy, repression - and build the data backbone that harmonizes modalities and platforms into something we can actually model on.
  • Sit shoulder-to-shoulder with wet lab teammates, closing the loop between predictions and screens: ingest fresh NGS readouts, retrain, re-prioritize, and pick the next experiment that teaches the model the most.

Core requirements
  • PhD in Bioinformatics, Computational Biology, or related quantitative field (or MS with 5+ years relevant industry experience);
  • Demonstrated track record applying computational TF ranking and GRN inference to cellular reprogramming problems, transdifferentiation, directed differentiation, or iPSC systems;
  • Multi-platform single-cell RNA-seq expertise: hands-on analysis from at least two different platforms, including platform-specific troubleshooting and quality control;
  • Multi-modal genomics proficiency: ChIP-seq, CUT&RUN, or ATAC-seq analysis including peak calling, differential accessibility, and TF motif enrichment;
  • Hands-on experience with established GRN inference methods to nominate or rank regulators of cell state, beyond literature-curated lists;
  • Experience analyzing pooled perturbation screens (CRISPRa, CRISPR knockout, or barcoded TF overexpression) with single-cell or bulk readouts;
  • Working knowledge of trajectory inference and pseudotime methods for mapping cell state transitions;
  • Strong programming skills in Python and R, with proficiency in Scanpy/Seurat and statistical analysis for high-dimensional data;
  • Comfortable working in a modern computational environment: cloud platforms, workflow managers, containerization, and collaborative version control;
  • Strong publication record and demonstrated cross-functional collaboration with experimental biologists.

You'll stand out with
  • Direct experience nominating or validating TF cocktails that successfully induced a cell state conversion (published or in preparation).
  • Experience with dynamical systems modeling for cell state transitions, or inverse problem approaches for TF combination ranking.
  • Background in advanced trajectory inference (optimal transport, GRN dynamics over pseudotime), Bayesian genomics, multi-omics integration, or cross-species comparative regulatory genomics.
  • Familiarity with transformer architectures in genomics and interest in hybrid classical/ML approaches to gene regulation.

Why e184?
  • Unrivaled impact: Your work directly enables technology that transforms human fertility and reproductive medicine.
  • Full-spectrum growth: Gain exposure to the entire lifecycle of discovery. From screening to mechanistic validation.
  • Best of both worlds: Experience the creative chaos of an early-stage startup with the stability of a well-capitalized company.
  • Elite collaboration: Work alongside a world-class team who are as driven as you are.

What we offer
  • Competitive salary + equity participation is considered
  • State-of-the-art facility in Portland metro area
  • Comprehensive Medical, Dental, Vision, and 401(k) with company match
  • 20 days PTO + 11 paid holidays

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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