Principal Machine Learning Engineer, Matching & Recommendations
UK London · London, United Kingdom
- Pay
- Pay not listed
- Work setting
On-site — work setting source
Location type: On-site
From the employer’s posting- Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting- Team
Engineering › Machine Learning — team source
Department: Engineering › Machine Learning
From the employer’s posting
Before you apply
- Sponsorship
Visa sponsorship not confirmed — sponsorship source
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From the employer’s application form
What you’ll work on
Full postingDefine the technical strategy for recommendation, matching and ranking systems
Design and deploy production models in PyTorch for high-traffic environments
Build production AI agents using LLMs and MCP integrations
Architect end-to-end ML pipelines with Spark and Airflow
Define experimentation strategy across A/B tests and offline evaluation
From the employer’s posting
Define and lead the technical strategy for AI and Machine Learning systems that power recommendations, matching, ranking and personalisation
Design, develop and deploy production-grade models using modern ML frameworks such as PyTorch
Build and deploy production AI agents using foundation models and fine-tuned Large Language Models (LLMs), alongside sub-agents, tools and MCP integrations
Architect end-to-end ML pipelines, integrating large-scale data processing technologies such as Spark and Airflow
Define and evolve experimentation strategies, including A/B testing, offline evaluation and online measurement
What you’ll bring
All qualificationsCore experience
- You have deep expertise in Machine Learning, with significant hands-on experience designing, building and deploying large-scale ML systems in production environments.
- You have strong proficiency in Python and at least one major ML framework, such as PyTorch or TensorFlow, with experience in areas such as recommendation systems, ranking, retrieval, personalisation or NLP.
- You have experience prompting and fine-tuning Large Language Models (LLMs) and building production AI agents using modern agentic architectures and tooling.
- You have proven experience designing scalable data and ML pipelines using technologies such as Spark, Airflow, or comparable distributed data and orchestration systems.
Qualification wording
You have deep expertise in Machine Learning, with significant hands-on experience designing, building and deploying large-scale ML systems in production environments.
You have strong proficiency in Python and at least one major ML framework, such as PyTorch or TensorFlow, with experience in areas such as recommendation systems, ranking, retrieval, personalisation or NLP.
You have experience prompting and fine-tuning Large Language Models (LLMs) and building production AI agents using modern agentic architectures and tooling.
You have proven experience designing scalable data and ML pipelines using technologies such as Spark, Airflow, or comparable distributed data and orchestration systems.
Tools in this posting
- Python
- Spark
- Airflow
- PyTorch
- TensorFlow
Source — Tool mentions in context
- You have deep expertise in Machine Learning, with significant hands-on experience designing, building and deploying large-scale ML systems in production environments. - You have strong proficiency in Python and at least one major ML framework, such as PyTorch or TensorFlow, with experience in areas such as recommendation systems, ranking, retrieval, personalisation or NLP. - You understand modern recommendation-system approaches and can reason deeply about model architecture, features, loss functions, evaluation methodology, experimentation and the trade-offs between offline performance and online outcomes.
- Build and deploy production AI agents using foundation models and fine-tuned Large Language Models (LLMs), alongside sub-agents, tools and MCP integrations. - Architect end-to-end ML pipelines, integrating large-scale data processing technologies such as Spark and Airflow with model training, evaluation, deployment and monitoring workflows. - Define and evolve experimentation strategies, including A/B testing, offline evaluation and online measurement, to continuously improve model performance and product outcomes.
- You have experience prompting and fine-tuning Large Language Models (LLMs) and building production AI agents using modern agentic architectures and tooling. - You have proven experience designing scalable data and ML pipelines using technologies such as Spark, Airflow, or comparable distributed data and orchestration systems. - You have demonstrated the ability to operate as a senior individual contributor, setting technical direction and influencing strategy across teams without relying on direct authority.
- Define and lead the technical strategy for AI and Machine Learning systems that power recommendations, matching, ranking and personalisation across Bumble products, delivering measurable improvements in member outcomes, engagement and safety. - Design, develop and deploy production-grade models using modern ML frameworks such as PyTorch, ensuring scalability, reliability and performance in high-traffic environments. - Build and deploy production AI agents using foundation models and fine-tuned Large Language Models (LLMs), alongside sub-agents, tools and MCP integrations.
About Bumble
is the parent company of Bumble Date, BFF, and Badoo.
In the employer’s words · Read in context · Company website ↗
Posting record
Report an errorActively hiringFirst seen by us 2 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Oct 27, 2026 · 2 days ago
- Recorded sightings
- 12
- Last seen by us
- Oct 30, 2026
- Employer says posted
- Oct 7, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
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Job description
At Bumble, we’re building the Love Company — a place where healthy, equitable relationships and friendships can start and grow. Machine Learning sits at the heart of that mission, helping us understand what makes a meaningful connection and powering the matching, recommendation and personalisation experiences that bring people together.
As our Principal Machine Learning Engineer, Matching & Recommendations, you’ll help define the technical and ML strategy behind the next generation of Bumble’s recommendation systems. You’ll remain hands-on while tackling complex problems across retrieval, ranking, personalisation, experimentation and two-sided marketplace optimisation, partnering across Engineering, Product, Data Science and Analytics to turn ambiguous member problems into scalable ML solutions. You’ll lead through technical influence, raise the bar for ML engineering across the organisation, and help us build systems that optimise not simply for engagement, but for better connections and meaningful member outcomes.
What you'll do
Define and lead the technical strategy for AI and Machine Learning systems that power recommendations, matching, ranking and personalisation across Bumble products, delivering measurable improvements in member outcomes, engagement and safety.
Design, develop and deploy production-grade models using modern ML frameworks such as PyTorch, ensuring scalability, reliability and performance in high-traffic environments.
Build and deploy production AI agents using foundation models and fine-tuned Large Language Models (LLMs), alongside sub-agents, tools and MCP integrations.
Architect end-to-end ML pipelines, integrating large-scale data processing technologies such as Spark and Airflow with model training, evaluation, deployment and monitoring workflows.
Define and evolve experimentation strategies, including A/B testing, offline evaluation and online measurement, to continuously improve model performance and product outcomes.
Partner cross-functionally with Product, Engineering and Data leadership to translate complex business and member challenges into impactful ML solutions, collaborating with purpose and influencing at senior levels.
Mentor and elevate senior individual contributors, fostering a culture of Excellence, Curiosity and continuous learning across Bumble’s ML community.
Take ownership of complex and ambiguous problem spaces, seeing initiatives through from insight to measurable impact whilst adapting approaches as new information emerges.
Champion responsible AI practices, ensuring fairness, transparency, privacy and member safety are considered throughout the design and operation of Machine Learning systems.
About you
Typically, you will have 10–15 years of relevant experience, although we welcome candidates with alternative backgrounds who can demonstrate equivalent skills, scope and impact.
You have deep expertise in Machine Learning, with significant hands-on experience designing, building and deploying large-scale ML systems in production environments.
You have strong proficiency in Python and at least one major ML framework, such as PyTorch or TensorFlow, with experience in areas such as recommendation systems, ranking, retrieval, personalisation or NLP.
You understand modern recommendation-system approaches and can reason deeply about model architecture, features, loss functions, evaluation methodology, experimentation and the trade-offs between offline performance and online outcomes.
You have experience prompting and fine-tuning Large Language Models (LLMs) and building production AI agents using modern agentic architectures and tooling.
You have proven experience designing scalable data and ML pipelines using technologies such as Spark, Airflow, or comparable distributed data and orchestration systems.
You have demonstrated the ability to operate as a senior individual contributor, setting technical direction and influencing strategy across teams without relying on direct authority.
You are comfortable navigating highly ambiguous technical and product problems, balancing immediate delivery with longer-term architectural and ML strategy.
You partner effectively across functions, collaborating with purpose and taking ownership of outcomes in complex organisational environments.
You have a track record of mentoring and developing other engineers, role-modelling Respect and Excellence whilst helping build inclusive, high-performing teams.
You bring strong AI fluency and technical judgement, with the ability to independently design, evaluate and optimise ML systems whilst guiding others in the responsible and effective application of AI.
About Us
Bumble Inc. is the parent company of Bumble Date, BFF, and Badoo. The Bumble platform enables people to build healthy and equitable relationships, through Kind Connections. Founded by Whitney Wolfe Herd in 2014, Bumble was one of the first dating apps built with women at the center and connects people across dating (Bumble Date) and friendship (BFF). BFF is a friendship app where people in all stages of life can meet people nearby and create meaningful platonic connections and community based on shared interests. Badoo, which was founded in 2006, is one of the pioneers of web and mobile dating products.
AI Fluency
AI is important to us. We’re excited by people who are curious and experimental, and who think thoughtfully about how AI can amplify their impact and outcomes.
We encourage you to use AI responsibly as you prepare your application. Please don’t use it to fabricate experiences or answer questions live in interviews. We care deeply about authenticity and want to understand your real skills, judgment and voice, because building a meaningful, genuine connection with you matters to us.
Final Compensation
Will be determined based on factors such as the selected candidate’s qualifications, relevant experience, skill set, and other job-related considerations.
Inclusion at Bumble Inc.
Bumble Inc. is an equal opportunity employer and we strongly encourage people of all ages, color, lesbian, gay, bisexual, transgender, queer and non-binary people, veterans, parents, people with disabilities, and neurodivergent people to apply. We're happy to make any reasonable adjustments that will help you feel more confident throughout the process, please don't hesitate to let us know how we can help.
In your application, please feel free to note which pronouns you use (For example: she/her, he/him, they/them, etc).
AI in Bumble Inc. Hiring
At Bumble, we may use AI tools to support parts of our recruitment process — such as helping us record, transcribe, and summarize conversations, and supporting job alignment by comparing resumes and job descriptions to highlight skills and potential roles that may be a good match. These tools help us work more efficiently and stay focused on you during our conversations. Importantly, all hiring decisions are made by people. AI is used only to support our team’s efficiency and improve the candidate experience — not to evaluate or decide on your candidacy. Participation in AI-supported interviews and conversations is completely voluntary and will not impact your candidacy. If you’d prefer to opt out, simply let your recruiter or interviewer know at the start of a call, or anytime during the interview or conversation. Summaries and related data are retained only as long as needed in line with our internal data retention policies. If at any point you’d like a transcription or summary deleted, please contact your recruiter directly.
Fraudulent Candidate Detection
Our applicant tracking system analyzes signals relating to device, IP, email, and phone data associated with each application to protect applicants and our hiring process from fraudulent applications. These signals provide indicators for internal review only and do not constitute a definitive determination of identity or intent. No applicant is rejected, advanced, or otherwise affected based solely on this analysis without human review
For further information on how we hold and manage your data, please refer to our Privacy Policy.
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London, United Kingdom
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