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Careers at Biographica

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🌽 Safeguarding the future of food<br><br>We’re on a mission to accelerate the development of more productive, sustainable, nutritious & climate-resilient food sources. 

How? By building the world’s first ML-driven target discovery platform for crop gene-editing. <br><br>🧬 From drug target discovery to crop target discovery<br><br>While gene-editing of crops is becoming ever more efficient, identifying which genes to edit and how remains a significant challenge. 

To overcome this bottleneck, we use cutting-edge deep learning to accurately and efficiently identify high value genetic targets for crop gene-editing. 

Our approach draws inspiration from recent advancements in the drug discovery space, incorporating LLMs, transformers and graph-based technologies to build a best-in-class discovery platform for plant sciences.<br><br>

About Us

Cecy studied Biological Sciences at Oxford University and has an MSc in ML from Imperial College London, specialising in graph-based biological modelling. At Biographica, Cecy leads strategy and business development, leveraging prior experience in drug manufacturing and supply chain at GSK as well as market access strategy consulting for gene-editing technologies.

Cecily Price

Dom has Masters degrees in Maths and Stem Cell Biology, as well as a PhD in Computational Genomics, from Cambridge. In his PhD, and later as an ML researcher at Relation Therapeutics, Dom applied graph ML to target discovery. He leads platform development at Biographica, drawing inspiration from drug discovery to design efficient target discovery methods for crop gene-editing.

Dominic Hall

Meet the Founders

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We’re driven by curiosity and clarity. That means asking real questions, challenging assumptions, and changing our minds when the evidence points somewhere new. We care more about learning than being right, and we share what we find along the way.

Seek the truth

Responsibility here is about showing up and making things happen. We take initiative, follow through, and we care about the outcome.

Own it&nbsp;

When something needs doing, we lean in, start small, and learn quickly. Speed matters, but only when it’s directed. We focus on what’s most important, make deliberate trade-offs, and move forward with clarity and purpose.

Act fast with intention

We work as a team, not a collection of individuals. That means helping each other succeed, communicating with honesty and respect, and making space for different voices to shape where we go, to get to the outcomes - for the company and for individuals.

Show up for each other

We’re here to solve real problems, not just ship features. We stay focused on work that makes a difference - ambitious, practical, and grounded in the impact it can have on the world.

Build what matters

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Our Values

<html><head></head><body><h2><span style="font-family: helvetica;">About us</span></h2>
<h3>🌽 Safeguarding the future of food</h3>
<div>Biographica is on a mission to accelerate the development of more productive, sustainable, nutritious &amp; climate-resilient food sources. To achieve this, we're building the world’s first ML-driven target discovery platform for crop gene-editing.</div>
<div>&nbsp;</div>
<h3>🧬 Target discovery for gene-editing</h3>
<div>While gene-editing of crops is becoming ever more efficient, identifying which genes to edit and how remains a significant challenge. To overcome this bottleneck, we use cutting-edge deep learning to accurately and efficiently identify high value genetic targets for crop gene-editing. Our approach draws inspiration from advancements in the drug discovery space, incorporating transformers, graphs &amp; causal-ML to build a best-in-class discovery platform for plant sciences.</div>
<div>&nbsp;</div>
<h3>🧪 Productising discovery</h3>
<div>We’ve spent the last two years building rigorous ML and bioinformatics foundations for gene discovery (curated plant databases, reproducible pipelines, bespoke models). Our next step is to turn those foundations into a unified, agent-native product that scientists can use end-to-end: specifying traits, curating datasets, generating long-lists, running deep-dive analyses, and publishing defensible evidence packs for customers. This role sits at the centre of that transition.</div>
<h3>👥 Team</h3>
<div>Led by co-founders Dom (CTO) and Cecy (CEO), we are now a team of 13, including 2 ML engineers, 2 data engineers, 3 bioinformaticians &amp; 2 experimental scientists. We primarily work together in person from our office in Spitalfields, London, 4 days per week. This role will be based in London, with close collaboration across ML, data and scientific product.</div>
<div>&nbsp;</div>
<h2>What we're looking for</h2>
<div>We’re hiring a full-stack software engineer to build the internal discovery product tying together workflows we have built and optimised over the last 2 years. You will translate real scientific practice into software: designing canonical product objects (project specs, curated datasets, longlists, evidence cards), implementing “scientist-in-the-loop” interfaces, and wiring together bespoke in-house models, bioinformatics tooling and agent workflows into a cohesive experience.</div>
<h3>Your first priorities will be:</h3>
<ul>
<li>Building the core product objects and minimal UX/UI that anchor discovery projects.</li>
<li>Turning our internally developed data curation tooling into a comprehensive workbench, connecting it to curated/QC’d dataset objects and onboarding workflows.</li>
<li>Shipping a usable internal Target-ID workflow that supports a near-term enterprise discovery project, while staying aligned to our long-term and internal discovery workflows.</li>
</ul>
<h3>Core responsibilities</h3>
<ul>
<li>Build and iterate on internal product UX for trait/project specification, dataset curation, interactive target selection, per-target evidence cards and review flows</li>
<li>Integrate existing scientific tooling (models, bioinformatics etc.) into one unified workflow</li>
<li>Working with our data team to design and implement the canonical data models / objects that represent discovery work in-product, with strong provenance and versioning.</li>
<li>Collaborate with scientific leads to ensure workflows match how discovery is actually done, and reduce context switching/noise for scientists.</li>
<li>Support customer-facing deliverables by ensuring outputs are easy to review, export, and defend.</li>
</ul>
<h3>Additional responsibilities</h3>
<ul>
<li>Working with founders and our internal product team to translate product requirements into roadmap for a unified trait discovery platform.</li>
<li>Improve developer/scientist experience around debugging, reviewing, and reproducing discovery runs.</li>
<li>Contribute to engineering standards around scientific traceability and human-in-the-loop checkpoints.</li>
</ul>
<h3>Core competencies</h3>
<ul>
<li>Strong full-stack or backend engineering background with a bias toward building clean, reliable internal tools.</li>
<li>Experience designing data models / schemas and building product surfaces on top of them.</li>
<li>Comfort operating in ambiguous product spaces and iterating quickly with end users.</li>
<li>Ability to bridge disciplines: you should be able to sit with plant scientists and ML engineers, understand the workflow pain, and translate that into software.</li>
<li>Solid Python skills; familiarity with modern web stacks (React/TypeScript or similar) or willingness to work across the stack as needed.</li>
<li>Strong attention to correctness, provenance, and reproducibility in systems that generate scientific claims.</li>
</ul>
<h3>Nice to have competencies</h3>
<ul>
<li>Experience building tools for scientists, biotech teams, or other high-integrity domains.</li>
<li>Familiarity with workflow/data orchestration (e.g., Dagster), model registries (e.g., MLFlow), or agent frameworks.</li>
<li>Experience with knowledge/evidence graph-style products or structured reporting systems.</li>
<li>Interest in plant biology, gene regulation, or crop improvement (not required, but helpful).</li>
</ul>
<h2><span style="font-family: helvetica;">Benefits</span></h2>
<ul>
<li style="font-family: helvetica;"><span style="font-family: helvetica;">Competitive salary &amp; equity options</span></li>
<li style="font-family: helvetica;"><span style="font-family: helvetica;">25 days annual leave &amp; option for 2 weeks work from anywhere policy</span></li>
<li style="font-family: helvetica;"><span style="font-family: helvetica;">Benefits package</span></li>
<li style="font-family: helvetica;"><span style="font-family: helvetica;">Career development opportunities as the company scales</span></li>
<li style="font-family: helvetica;"><span style="font-family: helvetica;">Ownership of ambitious, mission-driven work with real-world impact</span></li>
<li style="font-family: helvetica;"><span style="font-family: helvetica;">Vibrant, innovative &amp; supportive work environment with a committed team</span></li>
<li style="font-family: helvetica;"><span style="font-family: helvetica;">Access to conferences, events &amp; professional development resources</span></li>
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