Principal Scientist, Biologics Drug Discovery

Relation Therapeutics
London, United Kingdom
Yesterday
Job Type
Permanent
Work Pattern
Full-time
Work Location
On-site
Seniority
Lead
Education
Degree
Posted
11 May 2026 (Yesterday)

About Relation

Relation is a sector defining TechBio company developing transformational medicines, with technology at our core. Our ambition is to understand human biology in unprecedented ways, discovering therapies to treat some of life’s most devastating diseases. We leverage single-cell multi-omics from patient tissue, functional assays, and machine learning to drive disease understanding, from cause to cure.

We are scaling rapidly and building a team of exceptional individuals to push the boundaries of drug discovery. You will work in highly interdisciplinary teams where biology, computation, and engineering come together to solve complex problems that have not been solved before. Our state-of-the-art wet and dry labs in the heart of London are designed to accelerate this integration and translate insight into impact.

We are committed to building diverse and inclusive teams. Relation is an equal opportunities employer and does not discriminate on the basis of gender, sexual orientation, marital or civil partnership status, gender reassignment, race, colour, nationality, ethnic or national origin, religion or belief, disability, or age.

By joining Relation, you will help define how medicines are discovered and deliver meaningful impact for patients.

The opportunity

Relation is offering an outstanding opportunity for an Principal Scientist, Biologics Drug Discovery to bring deep biologics drug discovery knowledge to our growing pipeline. You will help shape how we choose targets, select modalities, design discovery cascades, and progress biologics candidates from early concept through to IND-enabling studies. As Relation grows its scientific and machine learning capabilities, you will act as a senior individual contributor and technical authority on biologics drug discovery, working alongside our computational, ML, and translational teams to translate biological insight into well-designed molecules with a credible path to the clinic. You will be a key technical voice in pipeline decisions and a trusted partner to colleagues across Drug Discovery and beyond.

Day to day, you will

  • Apply deep biologics drug discovery knowledge to shape program plans, modality choice, and discovery cascades, contributing to the wider strategy set by Drug Discovery leadership.

  • Drive biologics programs end-to-end — from target validation and hit identification through lead characterization, optimization, and developability triage — against agreed Target Product Profiles (TPPs).

  • Make informed modality decisions across the biologics landscape (antibodies, bispecifics, ADCs, fusion proteins, fragments) and ensure the right molecule is selected for the right target and indication.

  • Design and oversee fit-for-purpose discovery packages, combining cell-based functional assays, biophysical characterization (SPR/Biacore, BLI/Octet), and developability assessment to drive sound candidate selection.

  • Operate effectively in a matrixed pharma/biotech environment, partnering with Computational Biology, Machine Learning, CMC, DMPK, Toxicology, and Translational colleagues to de-risk candidates and progress them toward IND-enabling studies.

  • Manage externalised workstreams day-to-day: support CRO selection and oversee outsourced biologics production, in vitro pharmacology, and in vivo studies, ensuring data quality and timelines.

  • Represent biologics drug discovery in project teams and pipeline reviews; communicate scientific progress, data, and risks clearly to project leadership and broader cross-functional stakeholders.

  • Partner with our ML and computational biology teams to bring biologics drug discovery insight into how predictive and generative approaches are applied to target selection, molecule design, and lead optimization.

  • Contribute to the development and continuous improvement of biologics discovery workflows and standard operating procedures, ensuring data integrity, reproducibility, and readiness for regulatory milestones.

  • Contribute to Relation’s scientific culture by mentoring junior scientists in biologics drug discovery, supporting publications and conference visibility, and helping to shape the broader scientific identity of the biologics group.

Professionally, you will have

  • A PhD. in Molecular Biology, Immunology, Pharmacology, Cell Biology, Protein Engineering, or a related life-science discipline.

  • Hands-on drug discovery experience gained in a pharma or biotech setting, with a clear track record of contributing to biologics programs progressing through key discovery milestones (target validation, hit-to-lead, lead optimization, candidate selection).

  • Proven experience working in a matrixed pharma/biotech environment, partnering effectively across functions (e.g. CMC, DMPK, Toxicology, Computational/ML, Translational) and balancing multiple stakeholders.

  • Deep working knowledge of biologics drug discovery: how biology is translated into a molecule, how modalities are chosen, how discovery cascades are designed, and what data are needed at each stage to make sound progression decisions.

  • Familiarity with the biologics modality landscape (antibodies, bispecifics, ADCs, fusion proteins, fragments) and an informed view of when each modality is the right tool for the biology.

  • Hands-on experience with the data types that drive biologics drug discovery decisions, including cell-based functional and screening assays and biophysical characterization (SPR/Biacore, BLI/Octet).

  • Working experience of CRO management across biologics drug discovery activities (e.g. molecule production, in vitro pharmacology, in vivo studies).

  • A good understanding of biologics drug discovery end-to-end, including TPP-driven decision-making, developability considerations, and awareness of what is required to support IND-enabling activities.

  • Curiosity about how ML and computational approaches can complement traditional biologics discovery workflows, and an interest in shaping how those tools are deployed.

Bonus experience:

  • Familiarity with bone biology and bone-microenvironment models (osteoblast, osteoclast, osteocyte; mineral-binding or resorption assays).

  • Peer-reviewed publications in the biologics drug discovery space, prior experience mentoring junior scientists, and exposure to a TechBio or computationally-driven discovery environment are also welcome.

Personally, you:

  • Are comfortable working in a matrixed environment,balancing multiple stakeholders and contributing effectively across teams.

  • Takeownership of your work, proactively seek opportunities to contribute, and enable others to do their best work.

  • Communicate openly and directly, give and receive feedback constructively, and handle challenging conversations with respect.

  • Actively seek out diverse perspectives, build strong working relationships, and contribute to shared goals across teams.

  • Embrace challenges with openness and resilience, set high standards for yourself, and strive to deliver meaningful outcomes.

Working Style & Culture at Relation

At Relation, we operate in amatrixed, interdisciplinary environment, where impact is driven through collaboration across scientific, technical, and operational domains. We collaborate, and you will partner with colleagues across multiple teams and projects, contributing your expertise while aligning to shared company priorities. We work together and win together!The patient is waiting!

Recruitment Agencies

Please note that Relation does not accept unsolicited resumes from agencies. Resumes should not be forwarded to our job aliases or employees. Relation will not be liable for any fees associated with unsolicited CVs.

Relation is a committed equal opportunities employer.

Related Jobs

View all jobs
Spotlight

Machine Learning Engineer - National Security (Gloucestershire)

Mind Foundry Gloucester, Gloucestershire, United Kingdom
On-site Clearance Required
Spotlight

Senior ML Compiler Engineer

Fractile Bristol, United Kingdom

Senior Data Scientist – Statistical Genetics

Relation Therapeutics London, United Kingdom
Permanent

ML Research Engineer, London

Isomorphic Labs London, United Kingdom
On-site

Principal Research Scientist - AI Safety

Faculty AI London, United Kingdom
Hybrid Clearance Required

Principal Data Scientist

Faculty AI London, United Kingdom
Hybrid

Principal Research Scientist

PhysicsX London, United Kingdom

(Alignment) Research Engineer/Research Scientist - Red Team

AI Security Institute London, United Kingdom

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Where to Advertise Machine Learning Jobs in the UK (2026 Guide)

Advertising machine learning jobs in the UK requires a different approach to most technical hiring. The candidate pool is small, highly specialised and in demand across AI labs, financial services, healthcare, autonomous systems and consumer technology simultaneously. Machine learning engineers and researchers move between roles through professional networks, conference communities and specialist platforms — not general job boards where ML roles compete with unrelated software engineering positions for the same audience. This guide, published by MachineLearningJobs.co.uk, covers where to advertise machine learning roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

Machine Learning Jobs UK 2026: What to Expect Over the Next 3 Years

Machine learning has undergone a transformation that few technology disciplines can match. In the space of three years it has moved from a specialism sitting at the edges of most organisations' technology strategies to a capability that sits at the centre of them. The tools have changed, the expectations have shifted, and the range of industries treating machine learning as a core business function — rather than an experimental one — has expanded dramatically. For job seekers, this creates both opportunity and complexity in roughly equal measure. The machine learning jobs market of 2026 is significantly larger than it was three years ago, but it is also significantly more demanding. Employers have developed more sophisticated expectations, the technical bar for specialist roles has risen, and the landscape of tools, frameworks, and architectural patterns that practitioners are expected to know has broadened considerably. The candidates who will thrive over the next three years are those who understand where the discipline is heading — which specialisms are attracting the most investment, which technologies are reshaping what machine learning engineers and researchers are expected to build, and how the definition of a machine learning career is evolving beyond the model-building core toward a much wider range of roles across the full ML lifecycle. This article breaks down what the UK machine learning jobs market is likely to look like through to 2028 — covering the titles emerging right now, the technologies driving employer demand, the skills that will matter most, and how to position your career ahead of the curve.

New Machine Learning Employers to Watch in 2026: UK and Global Companies Driving ML Innovation

Machine learning (ML) has transitioned from a specialised field into a core business capability. In 2026, organisations across healthcare, finance, robotics, autonomous systems, natural language processing, and analytics are expanding their machine learning teams to build scalable intelligent products and services. For professionals exploring opportunities on www.MachineLearningJobs.co.uk , understanding the companies that are scaling, winning investment, or securing high‑impact contracts is crucial. This article highlights the new and high‑growth machine learning employers to watch in 2026, focusing on UK innovators, international firms with significant UK presence, and global platforms investing in machine learning talent locally.