Machine Learning Engineer (Recommendations)

Healf
City of London
3 weeks ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

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

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.