Cleo
Machine Learning Engineering Manager - Growth
How your CV stacks up
Upload your CV to see how well it fits this job role
?%
Machine Learning Engineering Manager - Growth
About Cleo
At Cleo, we're not just building another fintech app. We're on a mission to fundamentally change humanity’s relationship with money. We aim to create a world where everyone—regardless of background or income—has access to a hyper-intelligent financial advisor in their pocket.
Cleo is a rare success story: a profitable, fast-growing unicorn with over $300 million in annual recurring revenue (ARR) and growing over 2x year-over-year. This isn’t just a job—it’s an opportunity to join a team of brilliant, driven individuals passionate about making a real difference. We set an exceptionally high bar for talent, seeking individuals who are not only at the top of their field but also embody our culture of collaboration and positive impact.
If you’re driven by complex challenges that push your expertise, the opportunity to shape something truly transformative, and the potential to share in Cleo’s success as we scale, while growing alongside a company that’s growing rapidly—this may be your perfect fit.
Follow us on LinkedIn to stay updated with new product features and insights from our team.
About The Role
We’re looking for an exceptional ML Engineering Manager to lead the Machine Learning (ML) 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 systems driving revenue growth while maximising lifetime value (LTV) for every 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 (acquisition, channels, campaigns)
- Growth Personalisation (on-app prompts, offers, and recommendations)
Together, the team maximises revenue while protecting long-term health.
What We're Building
- ML-powered prompt recommender systems that decide which offer or action to show users
- Personalised messaging and incentive systems based on user context and history
- Incrementality testing to measure true causal lift from interventions
- Multi-armed bandits and online learning for near real-time optimisation
- Scoring and ranking systems balancing short-term revenue with long-term retention
Responsibilities
Lead ML Strategy & Delivery
- Own the ML roadmap for Growth, collaborating with PM and leadership to prioritise high-impact projects
- Lead design and delivery of systems that personalise prompts, offers, and messaging for individual users
- Drive continuous improvement across ML models (from concept to experiment to production)
Build & Mentor Your Team
- Recruit, onboard, and develop 3-5 ML Engineers (mix of independent contributors and aspiring managers)
- Foster a high-performing culture where engineers excel in their work
- Balance mentorship with accountability—push teams to ship clean, high-quality work quickly
- Support career growth and technical development, creating clear levelling pathways
Collaborate at Scale
- 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
- Translate ML impact clearly to leadership and the wider business
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Own Technical Excellence
- Review ML designs and code, ensuring quality without dépassing
- Guide architectural decisions on model serving, latency, and scalability
- Maintain (and improve) the team’s ML infrastructure and tooling
- Lead incident response when models or systems degrade in production
Drive Experimentation & Learning
- Champion a test-driven approach to ML—focusing on impact, not just accuracy
- Ensure robust experiment design, holdout groups, and statistical rigor
- Foster a learning culture where failures are dissected and shared
- Publish learnings (both internally and externally)
Requirements: What You Bring
You’re a strong technical leader with hands-on ML expertise, particularly in areas such as:
- Recommender systems & personalisation (ranking models, multi-armed bandits, candidate generation)
- Uplift modelling & incrementality testing (understanding causal impact and incremental lift)
- Ad targeting & auction systems (optimising bidding, audience selection, campaign performance)
- Marketing mix modelling (MMM) (attribution, channel contribution, budget allocation)
You have shipping ML products at scale, managing teams (preferably 3-5 engineers) and understand the balance between rigorous experimentation and speed to market. You care deeply about ship high-impact work with great vibes, and you’re genuinely excited by the technical challenges of personalisation and growth.
What Makes You a Good Fit:
Technical Depth
- You can code, debug, and review ML systems—you’re in the trenches, not just administrating.
- Growth mindset: You learn quickly, adapt, and challenge assumptions with data-driven decisions.
- No bullshit: Direct, honest, and pragmatic. Say what you mean, mean what you say.
- Cross-functional leadership: You translate ML complexity for business stakeholders. No silos in collaboration.
- User-centric: Obsess over real-world impact—retention, revenue, and LTV—not just raw metrics.
Want to see us talk about your experience?
If these resonate, we’ll take a deeper dive:
- You’ve built recommender or ranking systems at scale (Spotify, Netflix, Amazon, ad targeting).
- You’ve done causal inference work (incrementality testing, false-data exclusion).
- You’ve scaled teams through hypergrowth, balancing velocity and quality.
- You speak growth fluently—LTV, CAC, channel economics, retention analysis.
- You’ve open-sourced, published, or spoken about ML, sharing knowledge with the industry.
Criteria Overview
Technical Experience
- 5+ years in ML/Data Science, with 2+ years in leadership or senior IC roles
- Hands-on experience shipping ML products end-to-end (not brainstorming in notebooks)
- Expertise in personalisation, recommenders, or growth-scaled ML preferred
- Strong foundations in statistical inference, experimentation, causal reasoning
- Experience with:
- Model serving, A/B testing, production monitoring
- Python, SQL, cloud platforms (GCP, AWS)
- Core ML tooling (scikit-learn, XGBoost, TensorFlow/PyTorch)
Get help with your application
Your very own career expert that helps elevate your application to the next level.
Leadership Experience
- Track record building and scaling high-performing teams
- Comfortable with hiring, onboarding, and developing engineers (L2-L4+)
- Hands-on with technical feedback, code reviews, architecture guidance
- Balances autonomy with accountability—knows when to step in, when to empower
Mindset & Values
- Impact-driven: Focuses on real business outcomes, not isolated metrics.
- Curious & humble: Understands you don’t have all the answers and thrives on learning.
- A teacher’s heart: Enjoys developing others while continuing your own growth.
- Adaptable: Works across technical and business contexts.
- Clear communication: Explanation of complex concepts to all audiences—no sugar-coating.
Process
- Interview with Recruiter (30 mins)
- Technical Interview with Hiring Manager (30 mins)
- Python Programming Interview (45 mins)
- Whiteboard Interview (60 mins)
- Management Skills Interview (60 mins)
Benefits: Why Work Here?
We offer:
-
Competitive compensation (base + equity):
- Base: £150k–170k (London, Hybrid) / £140k–160k (Remote UK)
- Performant reviews every 3 years aligned with quarterly OKRs
-
Fast-growing, VC-backed unicorn:
- Funded by Balderton & EQT Ventures
-
Clear career growth:
- Be part of rapid investment in innovation, leadership opportunities
-
Flexibility:
- Adaptable schedules tailored to each person’s needs
- Globally distributed team—work where you thrive, meet in person occasionally
-
Performance rewards:
- Quarterly reviews and generous pay raises (high performers)
- Equity top-ups across all levels post-review
-
Workplace benefits:
- 25 days annual leave + holiday day bonus (up to 30 days after 4 years)
- 5-week paid sabbatical after 4 years
- UK: 6% matched pension scheme
- Private Medical Insurance via Vitality, dental cover, and life assurance
- Counselling & therapy support via Spill
- Enhanced parental leave
- Workplace nursery scheme
-
AI tools:
- Paid platform access (ChatGPT, Codex, Claude)
- Latest AI coding environments
-
Freedom & choice:
- Pick your work computer (Windows or Apple)
- Virtual & in-person socials—build communities inside and outside the office
-
Diversity & inclusion:
- We strongly encourage applications from people of colour, LGBTQ+ community, people with disabilities, neurodivergent individuals, parents, carers, and those from low socio-economic backgrounds.
App Access For UK & Europe
For iOS users: Try Cleo via TestFlight
By applying, you confirm all details are accurate, and the following holds:
- If false/misleading information is provided, the application may be rejected, an offer withdrawn, or employment terminated.
- Your data is processed in line with Cleo’s Candidate Privacy Notice.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
Skills
Location