Head of Artificial Intelligence

La Fosse
Cambridgeshire
19 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 Impact

Data Scientist

Reader in Artificial Intelligence

Reader in Artificial Intelligence (Machine Learning, NLP, Reinforcement Learning, and AI Security)

Reader in Artificial Intelligence

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.

AI Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Changing career into artificial intelligence in your 30s, 40s or 50s is no longer unusual in the UK. It is happening quietly every day across fintech, healthcare, retail, manufacturing, government & professional services. But it is also surrounded by hype, fear & misinformation. This article is a realistic, UK-specific guide for career switchers who want the truth about AI jobs: what roles genuinely exist, what skills employers actually hire for, how long retraining really takes & whether age is a barrier (spoiler: not in the way people think). If you are considering a move into AI but want facts rather than Silicon Valley fantasy, this is for you.

How to Write an AI Job Ad That Attracts the Right People

Artificial intelligence is now embedded across almost every sector of the UK economy. From fintech and healthcare to retail, defence and climate tech, organisations are competing for AI talent at an unprecedented pace. Yet despite the volume of AI job adverts online, many employers struggle to attract the right candidates. Roles are flooded with unsuitable applications, while highly capable AI professionals scroll past adverts that feel vague, inflated or disconnected from reality. In most cases, the issue isn’t a shortage of AI talent — it’s the quality of the job advert. Writing an effective AI job ad requires more care than traditional tech hiring. AI professionals are analytical, sceptical of hype and highly selective about where they apply. A poorly written advert doesn’t just fail to convert — it actively damages your credibility. This guide explains how to write an AI job ad that attracts the right people, filters out mismatches and positions your organisation as a serious employer in the AI space.

Maths for AI Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are a software engineer, data scientist or analyst looking to move into AI or you are a UK undergraduate or postgraduate in computer science, maths, engineering or a related subject applying for AI roles, the maths can feel like the biggest barrier. Job descriptions say “strong maths” or “solid fundamentals” but rarely spell out what that means day to day. The good news is you do not need a full maths degree worth of theory to start applying. For most UK roles like Machine Learning Engineer, AI Engineer, Data Scientist, Applied Scientist, NLP Engineer or Computer Vision Engineer, the maths you actually use again & again is concentrated in a handful of topics: Linear algebra essentials Probability & statistics for uncertainty & evaluation Calculus essentials for gradients & backprop Optimisation basics for training & tuning A small amount of discrete maths for practical reasoning This guide turns vague requirements into a clear checklist, a 6-week learning plan & portfolio projects that prove you can translate maths into working code.