Senior Product Manager - Machine Learning and AI

Wise
London
4 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Analyst

Senior Data Analyst

Data Engineering Manager

Senior Data Analyst

Principal Data Scientist

Senior Product Manager - Machine Learning and AI
Overview

Wise is a global technology company, building the best way to move and manage the world’s money.


Wise is a global fintech that aims to move money more efficiently and affordably for people and businesses worldwide, building an entirely new network for the world’s money.


About The Role

Our Machine Learning and Generative AI Platform teams are at the forefront of Wise's AI transformation. We're building the foundations that enable our entire organisation to harness the power of AI safely and effectively. Our ML Platform provides cutting-edge tools that turn data science ideas into production with minimal effort, while our GenAI Platform empowers all Wisers to leverage state-of-the-art generative AI through seamless integration, robust governance, and best-in-class developer experience.


We’re looking for a Technical Product Manager who can get their hands dirty. This isn't a role where you'll just write requirements – you'll prototype solutions, analyze complex datasets, and work shoulder-to-shoulder with our engineering teams to shape the future of AI at Wise. You'll navigate the rapidly evolving GenAI landscape while ensuring we move fast without compromising on security, privacy, or compliance.


This is a unique opportunity to drive AI adoption across a global fintech, where your technical depth will be as valuable as your product sense.


How We Work

We work differently and we’re proud of it. Our teams are empowered to solve the most urgent and relevant problems they see for our customers. We all share the responsibility of making Wise a success. We empower Wisers to make decisions and take ownership of how they work best. Teams and individuals have different needs – that’s why we have company-wide principles, and then our teams set their own guidelines.


Responsibilities


  • Drive adoption of our ML/GenAI infrastructure by identifying friction points through data analysis and shipping solutions that reduce time-to-production from weeks to days
  • Build and validate technical roadmaps using prototypes, SQL analytics, and hands-on experimentation with our stack (Sagemaker, MLflow, Ray, Bedrock)
  • Define success metrics and implement dashboards that track metrics from model performance to business impact


Balance Speed With Safety


  • Design governance frameworks that enable rapid experimentation while ensuring compliance - automating risk assessments and privacy checks
  • Partner with security to implement model monitoring and access controls that protect customer data without blocking innovation
  • Create cost optimization strategies backed by data, reducing ML infrastructure spend while scaling usage


Drive Strategic Technical Decisions


  • Evaluate and select AI vendors through hands-on technical assessment and ROI analysis
  • Work with engineering to define architecture that scales - from feature stores to multi-cloud inference
  • Enable 10x more teams to use AI by building self-service tools, clear documentation, and reusable components


Qualifications


  • 6+ years of experience as a Technical Product Manager, with hands-on experience building data or ML products
  • Ability to translate between the worlds of data science, engineering, compliance, and business stakeholders
  • Built prototypes or production features or internal tools
  • Exceptional communicator who can explain complex technical concepts to non-technical stakeholders
  • Thrives in ambiguity and can structure complex problem spaces into clear, measurable outcomes
  • Hands-on experience with data analysis tools (Python/pandas, Jupyter notebooks) and the ability to analyze large datasets
  • Track record of shipping technical products that balance user needs with platform constraints
  • Deep understanding of ML workflows—from data pipelines and feature engineering to model training and deployment
  • Ability to read and understand code to debug issues and contribute to technical discussions


Nice To Have


  • Experience with modern ML stack (MLflow, Airflow, Sagemaker, Ray, Bedrock or similar)
  • Hands-on experience with LLMs—prompt engineering, fine-tuning, or building RAG systems
  • Knowledge of streaming data systems (Kafka, Flink)
  • Experience with Kubernetes, Docker, and cloud infrastructure
  • Previous experience building developer platforms or API products


What We Offer


  • Starting salary: £88,000-£118,000 + RSUs
  • Wise Benefits


Interested? Find out more


  • The Wise Tech Stack, 2025 Edition
  • Our Application Security Journey
  • Platform Engineering KPIs
  • Internal Platform as a Product at Wise
  • Wise Engineering


For everyone, everywhere. Wise is international and celebrates differences. Inclusive teams help us live our values and progress in our careers. If you want to find out more about what it's like to work at Wise, visit Wise.Jobs.


Keep up to date with life at Wise by following us on LinkedIn and Instagram.


Seniority level


  • Mid-Senior level


Employment type


  • Full-time


Job function


  • Product Management and Marketing


#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.

Careers

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

Jobs

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

Careers Jobs

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.