Staff Machine Learning Engineer

Introduction to the role & team

At Bumble, our mission is to create a world where all relationships are healthy and equitable - and at the heart of that mission is our ability to build intelligent, trustworthy systems at scale. As part of the Machine Learning team, you’ll play a critical role in shaping how AI powers connection, safety, and personalisation across our products.

As a Staff Machine Learning Engineer, you’ll operate as a highly autonomous technical leader, owning large, complex problem spaces and driving end-to-end machine learning systems that influence product direction and user outcomes. You’ll role model our values of Excellence and Curiosity by elevating technical standards, mentoring others, and continuously pushing the boundaries of what’s possible with AI.

AI is central to how we evolve. In this role, you’ll not only build advanced ML systems, but also shape how they are designed, scaled, and responsibly applied across teams - embedding best practices that enable long-term impact.

What You'll do

Own and drive large-scale, end-to-end ML systems powering recommendations, ranking, and personalization, influencing product and technical direction

Design and architect scalable ML platforms and pipelines, integrating data processing, training, evaluation, and deployment (e.g. Spark, Airflow)

Build and optimise advanced models using modern ML frameworks (e.g. PyTorch), ensuring performance, scalability, and reliability in production

Lead the development of LLM-powered systems and AI agents, including prompt design, fine-tuning, and system integration

Establish best practices for experimentation, including robust A/B testing and evaluation methodologies

Collaborate cross-functionally at depth, partnering with Product, Engineering, and Data to shape strategy and deliver impactful ML solutions

Mentor and elevate engineers across the team, fostering a culture of Respect, ownership, and technical excellence

Take ownership of highly ambiguous problem spaces, seeing initiatives through from insight to impact while adapting with an agile mindset

About You

Typically requires 8–12 years of experience, though we welcome candidates with alternative backgrounds that demonstrate equivalent skills.

Deep expertise in machine learning, with significant experience building and scaling production-grade systems

Strong proficiency in Python and ML frameworks (e.g. PyTorch, TensorFlow), with experience in recommendation systems, ranking, or NLP

Expertise in designing scalable ML and data pipelines using distributed systems (e.g. Spark, Airflow or similar)

Experience working with LLMs and building production-ready AI-driven features or systems

Demonstrated ability to operate as a senior individual contributor, influencing technical decisions and elevating team capabilities…