Machine Learning Engineer (Recommendations)

Healf
City of London
3 weeks ago
Create job alert
Machine Learning Engineer (Recommendations)

Join us as a Machine Learning Engineer (Recommendations) at Healf.


About Us

Healf is an e‑commerce platform at the intersection of personalised health and curated wellbeing. We connect customers with the world’s most effective products across EAT, MOVE, MIND, and SLEEP, and we’re just getting started. Our culture is grounded in The Healf Standard—five principles that define how we work and win.



  • We Work Harder Than Anyone Else: Building something that improves lives takes long hours, grit, and sacrifice, but we thrive on it.
  • Never Settle: We challenge the status quo and push ourselves to be better every day.
  • Obsession Over Talent: Talent alone isn’t enough—relentless curiosity and a drive to grow set us apart.
  • The Healf Lifestyle: We live what we preach—our personal commitment to wellbeing fuels our professional productivity.
  • Stronger Together: Everyone owns their lane, but we run as a unit.

The Role

We’re looking for a Machine Learning Engineer – Recommendations to help build the foundation of Healf’s personalisation and intelligence platform. You’ll design, train, and deploy recommendation models that power dynamic merchandising, personalised discovery, and tailored health journeys across web, app, and beyond. This is a highly cross‑functional role working closely with Product, Data, and Engineering to turn raw data into real‑time insights and experiences. Over time, you’ll also contribute to developing predictive algorithms that help users make better health decisions—forming the intelligence layer of Healf’s long‑term vision: a wellbeing platform powered by AI and data. Experience with LLMs, embedding models, and applied AI systems will be highly valuable as we evolve towards conversational and contextual recommendation systems.


Where You’ll Make An Impact

  • Build and evolve Healf’s recommendation engine, driving personalised product discovery and dynamic merchandising across web and app.
  • Develop and deploy machine learning models that optimise product relevance, content ranking, and user engagement.
  • Partner with Product and Data teams to define and capture the signals that power our personalisation logic.
  • Contribute to the development of predictive algorithms that leverage data from Healf Zone, Helix, and user behaviour to anticipate customer needs.
  • Collaborate with Engineering to integrate ML systems into production pipelines and ensure scalable performance.
  • Experiment with LLM‑based retrieval and recommendation architectures.
  • Continuously measure, evaluate, and optimise model performance through experimentation and A/B testing.
  • Help shape the roadmap for Healf’s broader wellbeing intelligence platform—connecting data, health insights, and user intent.
  • Champion data quality, ethics, and compliance in all model design and deployment processes.

What You’ll Bring

  • 4–6 years of experience as a Machine Learning Engineer or Data Scientist, ideally within eCommerce, consumer tech, or recommendation systems.
  • Strong background in building and deploying ML models using Python, PyTorch, TensorFlow, or similar frameworks.
  • Proven experience with recommendation engines, ranking algorithms, or personalisation pipelines.
  • Familiarity with LLMs, embeddings, and NLP techniques for recommendation and content matching.
  • Proficient in SQL and data manipulation tools; experience working with modern data stacks (e.g., dbt, Snowflake, BigQuery).
  • Solid understanding of MLOps practices—model versioning, CI/CD, and production monitoring.
  • Comfortable working across product and engineering teams to translate business goals into model objectives.
  • Experience with experimentation, A/B testing, and performance measurement.
  • Curious, self‑directed, and excited to build the intelligence layer behind the future of personalised wellbeing.

Why Join Healf

  • Do your life’s best work: Build something that matters, with a team that moves fast and aims high.
  • Surround yourself with A+ talent: You’ll work with high‑performers who care deeply and raise the standard every day.
  • Be a builder: This isn’t a cog‑in‑the‑machine role. You’ll help shape our voice, culture, and growth.
  • Wellbeing is the lifestyle: From office yoga to Healf Zone insights, everything we do is rooted in our pillars: EAT, MOVE, MIND, SLEEP.
  • Premium Wellhub Membership: Unlimited entry to thousands of gym, yoga, & fitness studios.
  • Exclusive Healf Perks: 50% off all Healf products plus discounted Healf Zone blood testing.
  • Nest Pension: Secure your future with our pension contributions.
  • Wellbeing‑Focused Workspace: Incredible Hammersmith office with great natural lighting.
  • Team Connection: Annual company‑wide retreat to recharge and bond.

Seniority level

Mid‑Senior level


Employment type

Full‑time


Job function

Engineering and Information Technology


Industries

Retail


#J-18808-Ljbffr

Related Jobs

View all jobs

Machine Learning Engineer / MLOps Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

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.