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Senior ML Researcher — Genomics & Variant Effect

AI Engineer Full-time Europe / Americas time zones

Job details

Not specified Salary
Europe / Americas time zones Eligibility
Senior Experience
Full-time Employment

About this role

Role overview

A senior research role leading a genomics and variant-effect track, building machine learning systems that turn raw genomic data into clearly interpretable, decision-grade signal. The work spans regulatory genomics, somatic variant interpretation, mutational signatures, and protein or nucleic-acid language models, and bridges the gap between research code and operating pipelines. Publication is encouraged where it makes sense; impact is the headline metric.

Responsibilities

- Lead research streams covering variant-effect prediction, regulatory genomics, and mutational signatures. - Select, fine-tune, and where needed train models on top of modern genomic and protein language models. - Design end-to-end pipelines that move from raw sequencing data to annotated, ranked outputs. - Set the team's evaluation discipline, including held-out sets that mean something, calibrated uncertainty, and sensible baselines. - Partner with engineering to take pipelines from research environments into production. - Mentor junior researchers and review experimental design across the team.

Requirements

- PhD in computational genomics, bioinformatics, machine learning, or an adjacent quantitative field. - Strong record in variant-effect prediction, regulatory genomics, or related ML applied to nucleic-acid or protein sequence data. - Fluency with somatic variant-calling tooling and annotation ecosystems, and comfort working in a sequencing data stack. - Strong Python plus PyTorch or JAX, with willingness to use R when the analysis calls for it. - Mature software-engineering practices and a track record of shipping reproducible pipelines. - Clear written and spoken English.

Nice to have

- Experience with cell-free DNA or liquid-biopsy analyses, or longitudinal multi-omics data. - Familiarity with workflow systems such as Nextflow, Snakemake, or WDL. - Exposure to clinical-grade reporting frameworks and data-governance constraints.

Benefits and work setup

- Conference, publication, and collaboration support. - Flexible remote work across European and Americas time zones.

Skills detected in the listing

PythonInformation SecurityMachine Learning
Detected Oct 5, 2026
Last verified Oct 6, 2026

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