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 personalization 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, personalization, experimentation, and two-sided marketplace optimization, 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 organization, and help us build systems that optimize 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, ranking, and personalization across Bumble products, delivering measurable improvements in user engagement and safety - Design, develop, and deploy production-grade models using modern ML frameworks such as PyTorch , ensuring scalability and reliability in high-traffic environments - Build and deploy production AI Agents using raw and fine-tuned foundational Large Language Models (LLMs), along with sub-agents, tools, and MCP integrations - Architect end-to-end ML pipelines, integrating data processing (e.g. Spark, Airflow) with model training, evaluation, and deployment workflows - Drive experimentation frameworks, including A/B testing and offline evaluation, to continuously improve model performance and product outcomes - Partner cross-functionally with Product, Engineering, and Data leadership to translate business 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 the ML community - Take ownership of complex, ambiguous problem spaces, seeing initiatives through from insight to impact while adapting approaches with an agile mindset - Champion responsible AI practices, ensuring fairness, transparency, and user safety are embedded into all machine learning systems About You - Typically requires 10–15 years of experience, though we welcome candidates with alternative backgrounds that demonstrate equivalent skills. - Deep expertise in machine learning, with hands-on experience building and deploying large-scale systems in production environments - Strong proficiency in Python and at least one major ML framework (e.g., PyTorch, TensorFlow), with experience in areas such as recommendation systems, ranking models, or NLP - Expertise in prompting and fine-tuning Large Language Models (LLMs) and building production AI Agents - Proven experience designing scalable data and ML pipelines using tools such as Spark, Airflow, or similar distributed systems - Demonstrated ability to operate as a senior individual contributor, influencing technical strategy and decision-making without direct authority - Experience partnering effectively across functions, collaborating with purpose, and taking ownership of outcomes in complex organizational environments - A track record of mentoring and uplifting others, role modeling Respect and Excellence while building inclusive, high-performing teams - Strong AI fluency, with the ability to independently design, evaluate, and optimize ML systems, and guide 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 bumble.com.

Requirements

Must-haves

Define and lead the technical strategy for AI and Machine Learning systems that power recommendations, ranking, and personalization across Bumble products, delivering measurable improvements in user engagement and safety
Design, develop, and deploy production-grade models using modern ML frameworks such as PyTorch , ensuring scalability and reliability in high-traffic environments
Build and deploy production AI Agents using raw and fine-tuned foundational Large Language Models (LLMs), along with sub-agents, tools, and MCP integrations
Architect end-to-end ML pipelines, integrating data processing (e.g. Spark, Airflow) with model training, evaluation, and deployment workflows
Drive experimentation frameworks, including A/B testing and offline evaluation, to continuously improve model performance and product outcomes
Partner cross-functionally with Product, Engineering, and Data leadership to translate business 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 the ML community
Take ownership of complex, ambiguous problem spaces, seeing initiatives through from insight to impact while adapting approaches with an agile mindset
Champion responsible AI practices, ensuring fairness, transparency, and user safety are embedded into all machine learning systems
Typically requires 10–15 years of experience, though we welcome candidates with alternative backgrounds that demonstrate equivalent skills.
Deep expertise in machine learning, with hands-on experience building and deploying large-scale systems in production environments
Strong proficiency in Python and at least one major ML framework (e.g., PyTorch, TensorFlow), with experience in areas such as recommendation systems, ranking models, or NLP
Expertise in prompting and fine-tuning Large Language Models (LLMs) and building production AI Agents
Proven experience designing scalable data and ML pipelines using tools such as Spark, Airflow, or similar distributed systems
Demonstrated ability to operate as a senior individual contributor, influencing technical strategy and decision-making without direct authority
Experience partnering effectively across functions, collaborating with purpose, and taking ownership of outcomes in complex organizational environments
A track record of mentoring and uplifting others, role modeling Respect and Excellence while building inclusive, high-performing teams
Strong AI fluency, with the ability to independently design, evaluate, and optimize ML systems, and guide others in the responsible and effective application of AI

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningmlaiagilepython

Platform: ashby
Effort: quick