Machine Learning Engineering Manager, Cleo AI
Growth
£140-160k
+ equity
SQL
AWS
GCP
Python
Whiteboard
Tensorflow
Scikit-Learn
PyTorch
Senior and Expert level
Remote from UK
AI personal finance assistant
Posted today
AI personal finance assistant
501-1000 employees
FintechB2CArtificial IntelligenceBankingPersonal financeFinancial ServicesSaaS
Posted today
£140-160k
+ equity
SQL
AWS
GCP
Python
Whiteboard
Tensorflow
Scikit-Learn
PyTorch
Senior and Expert level
Remote from UK
501-1000 employees
FintechB2CArtificial IntelligenceBankingPersonal financeFinancial ServicesSaaS
Company mission
To change the world’s relationship with money.
Role
Who you are
- You're a strong technical leader with hands-on ML experience
- Recommender systems & personalization - ranking models, candidate generation, multi-armed bandits, contextual decision-making
- Uplift modelling & incrementality testing - understanding causal impact and incremental lift
- Ad targeting & auction systems - optimising bidding, audience selection, and campaign performance
- Marketing mix modelling (MMM) - attribution, channel contribution, budget allocation
- You've shipped ML products at scale, managed teams (ideally 3-5 engineers), and you understand the balance between rigorous experimentation and speed to market
- You care deeply about bringing good vibes while pushing the team to make it happen, and you're genuinely excited by the technical challenges in personalisation and growth
- Technical depth: You can code, debug, and review ML systems. You're not a pure manager, you're in the trenches with your team on high-impact projects
- Growth mindset: You learn at speed, adapt quickly, and aren't afraid to challenge assumptions with data. You see every project as a chance to level up the team's capabilities
- No bullshit: You're direct, honest, and pragmatic. You say what you mean and you mean what you say
- Cross-functional leadership: You can translate between ML complexity and business impact. You collaborate naturally with PMs, Data Engineers, and Analytics - no silos
- User-centric: You obsess over impact - not just model accuracy, but real-world outcomes like retention, revenue, and lifetime value
- You've built recommender or ranking systems at scale - Spotify playlists, Netflix recommendations, Amazon product ranking, Pinterest pins, TikTok feed, Twitter/X timeline. That context is gold
- You've done causal inference work - incrementality testing, uplift modelling, experimentation design. You understand the difference between correlation and causation
- You've managed through hypergrowth - you've scaled a team, navigated process changes, and kept quality high while shipping at velocity
- You have growth or marketing domain experience - you understand LTV, CAC, channel economics, attribution, and retention. You speak fluent "growth."
- You've open-sourced or published ML work - papers, blog posts, talks. You like to share knowledge
- 5+ years in ML/Data Science roles, with at least 2+ years in a leadership or senior technical IC capacity
- Hands-on experience shipping ML products end-to-end (not just notebooks) - ideally in personalisation, recommender systems, or growth
- Strong fundamentals in statistical inference, experimental design, and causal reasoning
- Production ML experience: model serving, latency optimisation, A/B testing, monitoring
- Comfortable with Python, SQL, and cloud platforms (GCP, AWS, etc.)
- Experience with typical ML stacks (scikit-learn, XGBoost, TensorFlow/PyTorch, or similar)
- Track record of building and scaling high-performing teams
- Comfortable hiring, onboarding, and developing engineers from L2 to L4+
- Experience giving technical feedback, code reviews, and architectural guidance
- Ability to balance autonomy with accountability - you know when to step in and when to empower
- Comfort navigating ambiguity and making decisions with incomplete information
- You genuinely care about impact - shipping models that drive real business outcomes, not just optimising metrics in isolation
- You're intellectually curious and humble - you don't have all the answers and you're excited to learn from your team
- You're a teacher and a learner - you enjoy helping others grow while continuing to develop your own skills
- You can operate effectively across technical and non-technical contexts - you translate for stakeholders without over-simplifying
- You have strong communication skills - you can explain complex ML concepts clearly, both to engineers and to non-technical partners
What the job involves
- We're looking for an exceptional ML Engineering Manager to lead the Machine Learning efforts across our Growth team - the squad responsible for making smart, personalised decisions about what each of our 4M+ users sees, when they see it, and how we optimise for long-term value
- You'll manage a team of talented ML Engineers and collaborate with Marketing Engineers, Product Designers, PMs, and Data Scientists to build the systems that drive revenue growth while maximising lifetime value (LTV) for every single user. This is a high-impact role where you'll directly influence how we grow, retain, and monetise our user base
- The Growth team spans two squads: Growth Marketing (handling acquisition, channels, campaigns) and Growth Personalisation (handling on-app prompts, offers, and recommendations). Together, we maximise revenue while protecting long-term health
- ML-powered prompt recommender systems that decide which offer or action to show each user
- Personalised messaging and incentive systems based on user context and history
- Incrementality testing to measure true causal lift from our interventions
- Multi-armed bandits and online learning to optimise in near real-time
- Scoring and ranking systems that balance short-term revenue with long-term retention
- Own the ML roadmap for Growth, working with the PM and leadership to prioritise high-impact projects
- Lead the design and delivery of systems that personalise prompts, offers, and messaging to individual users
- Drive continuous improvement across ML models, from concept to experiment to production
- Recruit, onboard, and develop 3-5 ML Engineers (mix of IC and growing managers)
- Create a high-performing culture where people want to do their best work
- Balance mentorship with accountability - push the team to ship quality work quickly
- Support career growth and technical development; create clear pathways for levelling up
- Work closely with Growth Marketing Engineering on infrastructure, experimentation, and deployment
- Partner with Product on feature prioritisation and user experience design
- Engage Analytics on metrics, instrumentation, and incrementality testing
- Communicate ML impact clearly to leadership and across the business
- Review ML designs and code; ensure quality without becoming a bottleneck
- Guide architectural decisions on model serving, latency, scalability
- Maintain (and improve) the team's ML infrastructure and tooling
- Lead incident response when models or systems degrade in production
- Champion a test-driven approach to ML - we measure impact, not just accuracy
- Ensure robust experiment design, holdout groups, and statistical rigor
- Build a learning culture where failures are dissected and shared
- Publish learnings - both internally (to other teams) and externally
Application process
- Interview with a Recruiter (30 mins)
- Interview with the Hiring Manager (30 mins)
- Python Programming Interview (45 mins)
- Whiteboard Interview (60 mins)
- Management Skills Interview (60 mins)
Our take
People are increasingly not satisfied with the products being offered by traditional banks, which have proven slow to react to the needs of younger consumers who expect more from their everyday experiences.
Cleo was one of the first companies to make great use of chatbots that sit within Facebook's messenger app. This chatbot-first approach and Gen Z user base have given it strong virality. It now has millions of registered users, 99% of which are based in the US, leading to a decision to take a year break from the UK market to focus on its US Gen Z customers. And after a successful year in the US its now eyeing up a return to the UK.
Its Series C funding will help the company to double down in the US, where it will make a significant increase in employee headcount, and enable Cleo to maintain and expand its service during an economic downturn.
Financial advice delivered via bots is a crowded space, so Cleo benefits from having a strong brand presence. It has gone for very bold, fun, Gen Z-focused messaging which helps it to stand out from competitors such as Emma, Plum, and Chip.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
Top investors
Company
Funding (last 2 of 6 rounds)
Jun 2022
$80m
SERIES C
Dec 2020
$44m
SERIES B
Total funding: $137.7m
Company benefits
- Meaningful equity with early exercise, plus top-ups on promotion
- Primary caregiver: 18 weeks at full pay / Secondary caregiver: 6 weeks full pay, 6 weeks half pay (dependant on tenure)
- Paid 1-month sabbatical every 4 years
- Comprehensive health, dental & vision insurance
- Learning & development training platform
- Mental health support via Spill
- Company-wide performance reviews every 4 months
- We’ll cover your OpenAI or Claude subscription
- Pension match up to 6%
- Life assurance
- 25 days annual leave + an extra day for every year you’re at Cleo (up to five accrued days)
Company values
- Make it Happen - Own the outcome. Lend a hand, take a hand. Fast execution.
- No bullsh*t - Intellectually honest. Go hard on problems, not people. Data-backed decision making.
- Learn at Speed - Jump in and figure it out. Dig deep, do it right. Learn what matters.
- Bring Good Vibes - Read the room, lift the room. Show up for others. Keep it real, keep it positive.
Company HQ
Finsbury, London, UK
Leadership
Barnaby Hussey-Yeo
(Founder & CEO)Previously a Data Scientist at Wonga, one of the companies that pioneered the smart use of data for loan decisions. Joined Entrepreneur First where Cleo was formed.
Diversity, Equity & Inclusion at Cleo AI
- We’ve set up an internal committee of truly passionate people that are dedicated to ensuring Diversity and Inclusion is embedded in how we all operate as a team. We agree on meaningful actions, and get on with implementing them. We have three main focuses right now, which are:
- 1) Improving SocioEconomic diversity across the company
- 2) Increasing the number of women in Leadership roles
- 3) More community outreach
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