Principal Machine Learning Engineer, Matching and Recommendations

Bumble is hiring a Principal Machine Learning Engineer to define and lead machine learning strategy for next-generation recommendation systems. This role is hands-on across retrieval, ranking, personalization, experimentation, and marketplace optimization, with responsibility for measurable improvements in engagement and safety.

Key Responsibilities

• Define and lead technical strategy for AI and machine learning systems powering recommendations, ranking, and personalization across Bumble products, with 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), including sub-agents, tools, and MCP integrations
• Architect end-to-end machine learning pipelines that connect data processing (for example, Spark and 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 needs into impactful ML solutions, influencing at senior levels
• Mentor and elevate senior individual contributors, fostering a culture of Excellence, Curiosity, and continuous learning within the ML community
• Own complex and ambiguous problem spaces, taking initiatives from insight to impact while applying an agile mindset
• Champion responsible AI practices by embedding fairness, transparency, and user safety into machine learning systems

Required Qualifications

• Typically requires 10 to 15 years of experience; alternative backgrounds demonstrating equivalent skills are welcome
• Deep expertise in machine learning with hands-on experience building and deploying large-scale production systems
• Strong proficiency in Python and at least one major ML framework (for example, PyTorch or TensorFlow), including experience with recommendation systems, ranking models, or NLP
• Expertise in prompting and fine-tuning LLMs, with experience building production AI Agents
• Proven experience designing scalable data and ML pipelines using tools such as Spark, Airflow, or similar distributed systems
• Ability to operate as a senior individual contributor, influencing technical strategy and decisions without direct authority
• Effective cross-functional partnership with purpose, taking ownership of outcomes in complex organizational environments
• Track record of mentoring and uplifting others, role modeling Respect and Excellence, and building inclusive, high-performing teams
• Strong AI fluency, including the ability to independently design, evaluate, and optimize ML systems and guide others in responsible and effective AI use

Technologies

• Python
• PyTorch
• TensorFlow
• Large Language Models (LLMs)
• MCP
• Spark
• Airflow
• A/B testing

Location and Work Setup

New York, NY (onsite)

Compensation

USD 345,000 - 410,000 per year

Final compensation will be determined based on qualifications, relevant experience, skill set, and other job-related considerations.

Inclusion at Bumble Inc.

• Bumble Inc. is an equal opportunity employer and strongly encourages people of all ages, color, lesbian, gay, bisexual, transgender, queer and non-binary people, veterans, parents, people with disabilities, and neurodivergent people to apply
• Reasonable adjustments are available throughout the process; applicants are encouraged to let the team know how support would help
• In the application, candidates may note their pronouns (for example, she/her, he/him, they/them)

AI Use in the Hiring Process

• Bumble may use AI tools to support parts of recruitment, such as recording, transcribing, summarizing conversations, and comparing resumes and job descriptions to highlight skills and potential role matches
• Hiring decisions are made by people; AI supports team efficiency and the candidate experience rather than evaluating or deciding candidacy
• Participation in AI-supported interviews and conversations is voluntary and will not impact candidacy
• Candidates who prefer to opt out can notify their recruiter or interviewer at the start of a call or at any point during the conversation
• Summaries and related data are retained only as long as needed under internal data retention policies
• Candidates can request transcription or summary deletion by contacting their recruiter

Fraudulent Candidate Detection

• The applicant tracking system analyzes signals relating to device, IP, email, and phone data to protect applicants and the hiring process from fraudulent applications
• Signals are used for internal review only and do not provide a definitive determination of identity or intent
• No applicant is rejected, advanced, or otherwise affected based solely on this analysis without human review