Team Lead - Computer Vision

La Fosse
Cambridge
1 month ago
Applications closed

Related Jobs

View all jobs

Data Science Team Lead - Flexible, Mentoring & ML on Azure

Data Science Manager - Lead Collaborative, High-Impact Team

Machine Learning Team Lead

Machine Learning Team Lead

Machine Learning Team Lead

Machine Learning Team Lead

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.

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.