Helical

AI-driven in-silico lab for drug discovery

Principal Scientist: Computational Biology/ML - Genomics

Quantitative Biology & BiostatisticsConfirmed live in the last 24 hours

Full-timeLondon, UKNo salary listedHybrid

About the Job

Requirements

A PhD in Computational Biology, Machine Learning, Bioinformatics, or a related field, with a strong focus on Genomics and machine learning, and at least 2 years of non-academic industry experience; alternatively, an MSc in a relevant field and at least 5 years of industry experience applying machine learning to Genomics problems.
Strong understanding of machine learning and artificial intelligence methods for biological data, including transformers, variational autoencoders, diffusion models, or classical machine learning.
Hands-on experience with modern machine-learning frameworks such as PyTorch or equivalent.
Leadership experience with small teams working toward defined roadmaps or projects.
Ability to translate complex science into clear, actionable insights for technical and non-technical stakeholders.
Comfort working in a fast-paced, high-iteration environment, moving quickly from prototype to experiment to insight.
Strong interest in building at the intersection of biology and machine learning.

Responsibilities

Provide scientific leadership across model evaluation and applied use cases, ensuring work addresses relevant challenges in drug development and translational research.
Serve as a key scientific partner to the research team.
Mentor and support junior colleagues.
Work closely with Product and Business Development to turn biology and machine learning into customer-focused deliverables.

Principal Scientist (Comp. Bio/ML) - Genomics

Helical · London, United Kingdom (Hybrid) · Full-time · Posted 2026-09-24

Workplace: hybrid

Department: Science

Description

We’re looking for a Principal Computational Scientist/Director to help translate our foundation models into impactful, real-world discovery workflows. In this role, you’ll provide scientific leadership across model evaluation and applied use cases, ensuring our work stays grounded in the most relevant challenges in drug development and translational research.

You’ll join as a senior individual contributor with leadership and strategy experience at a point where the the team scales. You will act as a key scientific partner to our research team, mentor and support junior colleagues, and work closely with Product and Business Development to turn cutting-edge biology + ML into deliverables that matter for customers.

Requirements

PhD in Computational Biology, Machine Learning, Bioinformatics, or a related field, with a strong focus on Genomics + ML, and 2+ years of industry (non-academic) experience OR MSc in a relevant field and 5+ years of industry experience applying ML to Genomics problems
Strong understanding of ML/AI methods for biological data (e.g., transformers, VAEs, diffusion models, classical ML)
Hands-on experience with modern ML frameworks such as PyTorch (or equivalent)
Leadership experience with small teams to work towards defined roadmaps or projects
Excellent communication skills — able to translate complex science into clear, actionable insights for technical and non-technical stakeholders
Comfortable in a fast-paced, high-iteration environment, moving quickly from prototype → experiment → insight
Strong passion for building at the intersection of biology and machine learning

Apply

Apply at Helical

About the Company

Helical

AI-driven in-silico lab for drug discovery

Helical builds a virtual AI lab that turns biological foundation models into reproducible, decision-ready in-silico workflows for pharmaceutical R&D. The platform has a Virtual Lab for biologists and translational scientists and a Model Factory for ML engineers, and both share data, models, and results to bridge computational predictions with biological validation. It enables target identification, biomarker discovery, and therapeutic design by running computational workflows that generate actionable insights which are validated experimentally. The company differentiates itself by integrating biology and machine learning in a collaborative platform that creates an evidence layer linking predictions to validation, and by working with top pharma customers to shorten discovery timelines from months to days.

Company size: 11-50
Company stage: Seed
Total funding: $12.4M
Headquarters: Luxembourg, Luxembourg
Founded: 2023

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