Engineering Manager - Machine Learning (Competitive + Equity) at Fast-scaling AI logistics platform

Jack & Jill
City of London
3 months ago
Applications closed

Related Jobs

View all jobs

Engineering Manager, Data & Machine Learning

Engineering Manager - MLOps & Analytics

Software Engineering Manager – Machine Learning

Machine Learning Engineering Manager, Gen AI

Product Manager AI (AI & Machine Learning)

On Senior Lead - Machine Learning Engineering

Engineering Manager - Machine Learning (Competitive + Equity) at Fast-scaling AI logistics platform

Lead the evolution of an AI platform, overseeing a talented ML engineering team. You will scale AI/ML capabilities, architect systems for real-world automation, and bridge technical and commercial considerations. This role involves defining ML Ops strategy and mentoring engineers, building foundational AI advantage for a global logistics intelligence platform.


Location: London, UK


Salary: Competitive + Equity


What you will do

  • Lead and scale machine learning teams to deliver cutting‑edge AI solutions.
  • Own the architecture, infrastructure, and pipelines for robust ML capabilities.
  • Collaborate with Product and Engineering leadership to embed AI deeply into the platform.

The ideal candidate

  • 5+ years of engineering experience with a strong focus on ML/AI and data infrastructure, plus 2+ years of technical leadership in fast‑scaling startups.
  • Expertise in architecting ML pipelines, LLM integrations, data lakes, and real‑time data processing.
  • Track record of hiring, developing, and inspiring high‑performing ML and Data teams.

To apply, speak to Jack, our AI recruiter. Visit the website, click "Speak with Jack", and log in with your LinkedIn profile. Talk to Jack for 20 minutes so he can understand your experience and ambitions. If the hiring manager would like to meet you, Jack will make the introduction.


#J-18808-Ljbffr

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.

Careers

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.

Jobs

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.

Education

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.