Team Lead - Computer Vision

La Fosse
Cambridge
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Lead Analyst - Data Science_ UK

Senior Data Scientist

Software Engineering Manager – Machine Learning

Senior Machine Learning Scientist

Data Scientist

Senior Recruitment Consultant - AI & Data Science - Manchester

Team Lead (Computer Vision) – Drug Discovery/Biotech start-up


  • Paying up to £150/160k
  • Hybrid near Cambridge – 3 days per week
  • Computer Vision


We are partnering with a fast-growing, venture-backed biotech at the cutting edge of drug discovery. Their mission is leveraging next-generation sequencing, high-resolution imaging, and advanced machine learning to transform the drug-discovery process for cell revolution.


As they scale their AI capabilities, they are hiring a Team Lead (Computer Vision) to lead the development of new ML tools, drive scientific impact, and shape the company’s long-term AI strategy.


The Role

As a Team Lead you will lead a cross-functional team building the machine learning and computational platforms that power the company’s target discovery engine. You will work at the intersection of genomics, computer vision, and deep learning, collaborating closely with wet-lab scientists, computational biologists, and data scientists/ML engineers.


This is an end-to-end leadership role combining technical credibility with strategic direction. You will define the AI roadmap, guide the design of models and pipelines, support downstream scientific decision-making, and ensure the team is executing effectively.


The ideal candidate blends technical depth with pragmatism, scientific curiosity, and the ability to collaborate across disciplines.


Key Responsibilities

  • Lead and grow an AI/ML group currently consisting of 4/5 engineers/scientists, with planned expansion.
  • Own the strategic direction for AI across genomics, imaging, target discovery, and computational modelling.
  • Develop ML tools that process large-scale sequencing data, cellular imaging, and multimodal datasets.
  • Partner with computational biology and wet-lab groups to integrate AI models into scientific workflows.
  • Prioritise the roadmap and ensure delivery of high-impact internal tools and models.
  • Drive innovation across deep learning, computer vision, and emerging LLM applications.
  • Balance hands-on technical contribution (~10–20%) with leadership (60%) and long-term strategy (20–30%).


What I’m Looking For - Must-have experience:

  • Strong industry background in genomics, computational biology, or bio/pharma ML.
  • Proven experience applying deep learning and computer vision (e.G., segmentation, histology imaging).
  • Deep understanding of sequencing data, somatic variation, or related biological domains.
  • Leadership experience managing high-performing ML or data science teams.


Nice-to-have:

  • Exposure to LLMs and modern foundation-model approaches in biology.
  • Experience in early-stage biotech or building ML systems from scratch.


If you’re interested in this role and feel you hit the requirements, please apply to find out some more information.


Head of AI – Drug Discovery/Biotech start-up

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.