---
title: 'Member of Technical Staff, Applied AI at Latent Labs'
canonical: 'https://feeny.ai/job/member-of-technical-staff-applied-ai-latent-labs-london-3243a7h9ey2m'
type: 'job'
last_seen: '2026-09-10'
---

# Member of Technical Staff, Applied AI at Latent Labs

- **Company:** Latent Labs
- **Location:** London, United Kingdom
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-02-20
- **Last confirmed live:** 2026-09-10
- **Apply:** https://jobs.ashbyhq.com/latentlabs/21821a19-0f7a-4352-abe2-ac788fc33e0d/application

**Skills:** Generative Modelling, Machine Learning, Python, API Development, Cloud Computing, Model Serving, Version Control, Cloud Hardware Optimization, Computational Biology, Protein Design, Enterprise Software Development, Scientific Workflow Optimization

> Develop, deploy, and optimize generative AI models for customer environments, particularly in pharmaceutical and biotech sectors. The role involves collaborating with interdisciplinary teams to translate deep technical knowledge into production-grade API integrations and model-serving infrastructure.

## Job description

Member of Technical Staff, Applied AI The opportunity We are looking for a Member of Technical Staff with deep expertise in generative modelling to work at the interface between our frontier models and the customers who depend on them. 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 develop an in-depth understanding of our proprietary generative models and apply that knowledge to deploy, adapt and optimise them within customer environments - particularly in the pharmaceutical and biotech sectors. This is a hybrid role. You will need a researcher’s depth of understanding of our models, combined with the pragmatism and communication skills to translate that understanding into production systems that deliver scientific value for our partners.

## Who we are

At Latent Labs, we are building frontier models that learn the fundamentals of biology. We pursue ambitious goals with curiosity and are committed to scientific excellence. Before building Latent Labs, our team co-developed DeepMind’s Nobel-prize winning AlphaFold, invented latent diffusion, and built pioneering lab data management systems as well as high throughput protein screening platforms. At Latent Labs you will be working with some of the brightest minds in generative AI and biology. Our team is committed to interdisciplinary exchange, continuous learning and collaboration. Team offsites help us foster a culture of trust across our London and San Francisco sites. We’re looking for innovators passionate about tackling complex challenges and maximizing positive global impact. Join us on our moonshot mission.

## Who you are

- You are a strong ML researcher with experience in generative modelling. You have 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 deep understanding of generative model architectures, training dynamics and inference behaviour.
- You are a skilful 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 building systems that serve large models via APIs and running inference on cloud hardware, parallelising data and models across accelerators.
- You are customer-facing and delivery-oriented. You thrive in environments where customer success is the primary measure of your work. You can translate complex technical concepts into clear language for scientific and non-technical stakeholders alike.
- You are passionate about model performance. You have an detailed understanding of how ML libraries interplay with hardware and data and love to optimise deep learning models for training and inference speed. You use this knowledge to ensure that customer deployments are performant, cost-effective and reliable.
- 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 where you must context-switch between deep technical work and customer-facing engagements.

What sets you apart (preferred, not required)

- You have experience in computational biology or protein design. You have worked on ML-driven projects in biology and understand the unique data challenges, evaluation paradigms and scientific workflows of biological modelling.
- You have built production enterprise software. You have experience delivering software that meets enterprise-grade requirements for security, compliance, auditability and uptime.
- You have a natural science background. You are academically trained in physics, biology, chemistry or other related fields, giving you an intuitive understanding of the scientific problems our customers are solving.

## Your responsibilities

Develop, deploy and adapt our models for customer environments:

- Develop a deep working understanding of our generative models - their architectures, training data, capabilities and limitations.
- Collaborate in a joint codebase with other research scientists, engineers and protein designers, maintaining highest code standards.
- Drive the end-to-end technical deployment of Latent Labs models into customer environments, designing production-grade API integrations and model-serving infrastructure.
- Adapt and fine-tune models to meet specific customer requirements, collaborating closely with our research team to ensure scientific rigour.
- Build ML data pipelines for customer-specific inference, evaluation and feedback workflows.
- Ensure deployments meet customer standards for security, performance and reliability.

Customer partnership & product feedback:

- Work embedded with pharmaceutical and biotech partners to scope technical requirements, troubleshoot issues and deliver solutions.
- Serve as the technical point of contact for assigned customers, building trusted relationships with their scientific and engineering teams.
- In collaboration with customer biology teams, plan and carry out model inference against biological targets. Quickly learn from results and feed insights back to our models.
- Gather and synthesise customer feedback, translating it into actionable insights for our product, research and platform teams.
- Create technical documentation, integration guides and best-practice resources.
- Spend time working on-site at international partner locations as needed.

Self development:

- Stay on top of the latest developments in ML, model serving and cloud-native tooling.
- Gain a strong working understanding of protein and cell biology.
- Collaborate in a joint codebase with other research scientists, engineers and protein designers, maintaining highest code standards.
- Participate in knowledge sharing, e.g. organise 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 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. [jobs.ashbyhq.com](https://jobs.ashbyhq.com/latentlabs)

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