--- title: 'Machine Learning Scientist at Latent Labs' canonical: 'https://feeny.ai/job/machine-learning-scientist-latent-labs-london-9m8twmtyrvpj' type: 'job' last_seen: '2026-09-10' --- # Machine Learning Scientist at Latent Labs - **Company:** Latent Labs - **Location:** London, United Kingdom - **Employment:** full-time - **Work type:** hybrid - **Posted:** 2026-06-29 - **Last confirmed live:** 2026-09-10 - **Apply:** https://jobs.ashbyhq.com/latentlabs/110a5e8d-bb5b-4148-a398-97f025feb53b/application **Skills:** Generative modeling, Machine learning, Deep learning, Python, Version control, Cloud hardware, Parallelization, Data pipelines, Model optimization, Computational biology, Protein design, ML-driven projects in biology > Join the Foundation Models team to architect novel generative models for protein design. You will build ML pipelines, optimize deep learning models, and collaborate with biologists to translate wet lab results into improved generative models. ## Job description The Opportunity We are looking for a highly skilled machine learning researcher with significant experience in generative modeling. You will join an interdisciplinary team of machine learners, protein engineers and biologists, jointly working to change the way that we control biology and cure diseases. In your role you will architect novel generative models with the goal of designing new proteins that are functional in wet lab assays. ## Who you are - You are a strong ML researcher with experience in generative modeling. You have led or worked on notable machine learning projects, as documented by your contributions to widely used open source libraries, significant product launches or high impact publications, e.g. at NeurIPS, ICML, ICLR or Nature venues. You have a proven track record of deep expertise in generative modeling. - You are a skillful ML developer. You write ML code that is robust, tested and easy to maintain. You have experience using version control and code review systems. You are a fast prototyper and hacker who can also write beautiful production code. You have experience running training and inference on cloud hardware, parallelizing data and models across accelerators. - You are a data engineer. You have experience building ML data pipelines for the training and evaluation of deep learning models. You are able to analyze the raw data, construct appropriate dataset splits and build pipelines that perform and scale. - You are passionate about model performance. You have an intricate understanding of how ML libraries interplay with hardware and data and love to optimize deep learning models for training / inference speed. You have a deep knowledge of best principles and tricks in architecting deep learning models and use it to optimize how they perform on validation metrics. - You are mission driven and curious. You are passionate about making a positive impact on the world, whether it's for patients, customers or beyond. You are motivated by the end goal and are flexible in adapting to different approaches and methodologies. You are curious about problems, however small or big they appear. You thrive in a dynamic environment. You work well in a fast-paced setting where goals must be achieved efficiently and urgently. What sets you apart - You have experience in computational biology or protein design. You have worked on ML-driven projects in biology. - You have a natural science background. You are academically trained in physics, biology, chemistry or other related fields. ## Your Responsibilities - Build machine learning models that work in the physical world [~90% of your time]: - Contribute to a careful curation of our training and evaluation data. - Propose and build ML evaluation metrics that align with real world success and company goals. - Quickly prototype generative models against our lead metrics and perform deep analyses of improvements. - Collaborate in a joint codebase with other research scientists, engineers and protein designers, maintaining highest code standards. - Contribute to the maintenance of our compute and ML development infrastructure. - In collaboration with the bio team, plan wet lab testing campaigns and carry out model inference against biological targets to enable their testing in the wet lab. - Quickly learn from wet lab results and feedback data to our models. - Self development [~10% of your time]: - Stay on top of the latest developments in ML. - Gain a strong working understanding of protein and cell biology. - Participate in knowledge sharing, e.g. organize and present at our internal reading group. - Attend and present at conferences. Apply We offer strongly competitive compensation and benefits packages, including: - Private health insurance - Pension/401(K) contributions - Generous leave policies (including gender neutral parental leave) - Hybrid working - Travel opportunities and more We also offer a stimulating work environment, and the opportunity to shape the future of synthetic biology through the application of breakthrough generative models. We welcome applicants from all backgrounds and we are committed to building a team that represents a variety of backgrounds, perspectives, and skills. ## About Latent Labs ## Company Overview - **One-liner**: Latent Labs is a frontier AI lab building generative models that capture the fundamentals of biology to make biology programmable and transform health and sustainability. - **Entity Type**: Private (Series A) - **Headquarters**: London, United Kingdom (Old Street Yard, White Collar Factory) - **Founded**: 2023 - **Founders**: Simon Kohl (CEO) ## Core Business - Primary industry/industries: Biotechnology Research, Artificial Intelligence - Target customers: B2B, partnering with pharmaceutical and biotechnology companies to empower them with breakthrough generative AI for molecular biology - Mission or purpose statement: "Making biology programmable to transform health and sustainability for the benefit of all." ## Products & Services - **Latent-X**: An atom-level frontier model for *de novo* protein binder design, enabling the creation of new proteins from scratch. - **Latent-X2**: A frontier model specifically designed for drug-like antibodies with low immunogenicity in human panels. - **Latent-Y**: The first lab-validated agent for drug design, integrating AI with real-world wet-lab validation to close the design-build-test loop. - **Partner Empowerment**: The company focuses on empowering partners with generative AI to gain unprecedented control over molecular biology, including creating new antibodies, optimizing existing enzymes, and advancing genetic engineering. ## Market Standing - **Valuation/Market Cap**: Not publicly disclosed - **Total Funding**: $99.1 million (across 4 rounds) - **Latest Round**: Series A ($40 million, led by Sofinnova Partners and Radical Ventures, closed March 2025) - **Notable Investors/Partners**: Radical Ventures, Sofinnova Partners, 8VC, Pillar VC, Kindred Capital, Isomer, Flying Fish. Angel investors include Google Chief Scientist Jeff Dean, Anthropic CEO Dario Amodei, and ElevenLabs founder Mati Staniszewski. - **Growth Signals**: Headcount grew +31.2% year-over-year (19 employees); highly active hiring with 11 open positions; launched three frontier models (Latent-X, Latent-X2, Latent-Y) in rapid succession; founding team co-developed DeepMind’s Nobel-prize winning AlphaFold models. ## Competitive Advantages - **Talent Moat**: Founding team pioneered programmable biology at DeepMind, having co-developed the Nobel Prize-winning AlphaFold models, giving them world-leading expertise in computational biology. - **Full-Stack Integration**: Unique ability to combine frontier generative AI with in-house wet-lab validation (as demonstrated by Latent-Y), creating a closed-loop design-build-test capability that pure-play AI labs or traditional biotechs cannot easily replicate. - **Interdisciplinary Culture**: Explicitly fosters deep engagement between AI and biology teams, enabling innovation at the intersection of both fields. ## Strategic Focus - **Current priorities**: Continuing to develop and deploy frontier generative models for all molecules of life; expanding partnerships with pharmaceutical and biotech companies; growing the team across AI, biology, and operations functions. - **Direction**: Empowering external partners with their AI models rather than becoming a therapeutics company themselves, believing their biggest impact will be in enabling others. ## Why Work Here - **Culture**: Described by employees as highly interdisciplinary, with deep engineering excellence and a focus on "magic" at the intersection of AI and biology. The team is small (~19 people) and growing, offering significant ownership and impact. - **Remote/Hybrid/Office Policy**: Offices in London (HQ) and San Francisco, with team members also in Spain and Canada. Specific remote/hybrid policy not publicly detailed but presence in multiple locations suggests flexibility. - **Notable Perks/Engineering Culture**: Employer rating of 5.0/5.0 on LinkedIn (from 1 review), with perfect scores for Work-Life (5.0), Compensation (5.0), Culture (5.0), and Career (4.0). The team includes talent from Google DeepMind, Altos Labs, the Institute for Protein Design, and other top AI and biotech organizations. The company is building tools that enable the "impossible" in biology, working on moonshot-level problems. ## Sources 1. [latentlabs.com](https://www.latentlabs.com/) 2. [linkedin.com](https://www.linkedin.com/company/latent-labs) 3. [cbinsights.com](https://www.cbinsights.com/company/latent-labs-technologies) 4. [latentlabs.com/our-dna](https://www.latentlabs.com/our-dna/) 5. 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