AI Engineer, Personalisation & Recommendations

Calyptus

Greater London

On-site

GBP 75,000 - 120,000

Full time

3 days ago
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Job summary

Calyptus is recruiting for a role where you will design and productionise recommendation and personalization algorithms for a consumer-facing platform. You will collaborate with a Data Engineer and a small senior team to ship model-driven features into production.

You’ll own experiments, select valuable signals, and apply agentic/LLM approaches to improve outcomes. This position targets candidates with a strong ML/Data Science background and experience at consumer platforms.

Qualifications

  • Experience building production-ready recommendation or personalization systems.
  • Strong ML/Data Science foundation beyond API usage.
  • Experience with consumer platforms and large-scale data.

Responsibilities

  • Design and productionise recommendation engines and personalization algorithms for a consumer-facing platform.
  • Take models from experiment to production and own their performance.
  • Decide which signals matter and work with the Data Engineer to model and expose them.
  • Run experiments and measure real impact on user behaviour, not just offline metrics.
  • Apply modern agentic and LLM-based approaches where they improve outcomes.

Skills

Recommendation systems
Machine Learning
Data Science
Independent contributor

Job description

This role is being recruited by Calyptus on behalf of one of our clients.

Calyptus is a specialist recruitment partner helping startups and high-growth technology companies hire across engineering, product and design.

What to expect
  • We’ll review your application and, if your experience is a strong match, one of our team will be in touch
  • We’ll support you throughout the interview process and keep you updated at each stage
  • If this particular role isn’t the right fit, we may consider you for other relevant opportunities with our clients

______________________________________________________

You’ll own the algorithms that decide what every user sees. That means designing, building and shipping recommendation and personalisation systems end-to-end — from problem framing and feature design through to production deployment, monitoring and iteration. You’ll work alongside a dedicated Data Engineer who builds the pipelines and architecture underneath you, and you’ll report into a small, senior team where your work goes live quickly.

What you'll do
  • Design and productionise recommendation engines and personalisation algorithms for a consumer-facing platform
  • Take models from experiment to production, and own their performance once they’re there
  • Decide what signals matter, and work with the Data Engineer to get them modelled and available
  • Run experiments and measure real impact on user behaviour, not just offline metrics
  • Apply modern agentic and LLM-based approaches where they genuinely improve outcomes
What we're looking for
  • Demonstrable experience building and productionising recommendation systems or personalisation algorithms for apps or websites — shipped, live, used by real users
  • A genuine Machine Learning and Data Science foundation — you were solving these problems before the ChatGPT era, not just calling APIs
  • Background from a consumer platform where behavioural data is the core asset — social platforms, marketplaces, travel, large-scale consumer tech
  • Comfortable operating as an IC in a small team with high autonomy
  • AI-native in practice: fluent with agentic tooling and able to use it to move faster
Nice to have
  • Experience applying agent-based approaches to recommendations or personalisation
  • Experience at a company that scaled from Series B through C/D/E

______________________________________________________

Genuine opportunities

Every role advertised by Calyptus is a live position we’re actively recruiting for. Some clients prefer to remain confidential during the early stages of the hiring process. When that’s the case, certain company details may be shared later in the process.

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