Head of Artificial Intelligence

La Fosse
Cambridgeshire
13 hours ago
Create job alert
Overview

Sector: Biotechnology / Genomics / Drug Discovery

La Fosse have exclusively partnered with an early-stage, privately funded biotechnology company building a next-generation genomics and therapeutics platform focused on diseases with significant unmet need. The company is part of a highly respected venture-backed ecosystem that specialises in creating and scaling category-defining life sciences businesses.

Our mission is to invent and operationalise breakthrough technologies that combine advanced biology, data, and machine learning to fundamentally change how targets are discovered, validated, and translated into medicines.

They are looking for a Head of AI to define, build, and productionise the machine learning capabilities that sit at the core of our platform and drug discovery engine. This is a senior director role with hands-on leadership and significant influence over technical strategy, team growth, and scientific direction.

You will work closely with leaders across assay development, data infrastructure, biology, and drug discovery, operating in a highly collaborative, low-ego environment. The role combines deep technical ownership with real-world biological impact.

Key Responsibilities
  • Define and own the AI/ML strategy and roadmap across platform development and drug discovery
  • Lead, mentor, and grow a high-performing AI/ML team, setting standards for quality, rigor, and reproducibility
  • Partner with data infrastructure teams to optimise data schemas, metadata, versioning, and access controls
  • Develop and deploy ML methods leveraging next-generation sequencing (NGS) for target discovery and validation
  • Build and productionise imaging AI capabilities (e.g. histology / microscopy), including segmentation, detection, classification, and weak or self-supervised learning approaches
  • Apply AI to accelerate target validation and drug development, tightly linking models to biological outcomes
  • Communicate complex technical results clearly to both technical and non-technical stakeholders
What We’re Looking For
  • Strong industry experience in genomics, biotechnology and/or drug discovery
  • 7+ years of industry experience delivering AI/ML systems end-to-end in drug discovery or target identification
  • Strong leadership experience in a matrixed, collaborative environment
  • Credible AI technical background.
  • Someone who is driven by innovation.
Priority domain experience
  • Genomics / NGS-based ML methods
  • AI applied to target validation and/or drug discovery (any modality)
Why Join
  • Opportunity to shape AI strategy at the core of a new therapeutic platform
  • High ownership, high impact role in an innovation-led, early-stage environment
  • Close collaboration with world-class scientists and technologists

If this role sounds of interest, please apply through the AD to find out more!


#J-18808-Ljbffr

Related Jobs

View all jobs

Head of Artificial Intelligence

Head of Artificial Intelligence

Head of Artificial Intelligence

Global Head of Artificial Intelligence, ERM

Teacher of Computer Science & Artificial Intelligence Lead

Artificial Intelligence Co-Founder / Head of Sales (100 % remote) (m/f/d)

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

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

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.