Machine Learning Engineer

Inclusion at Bumble Inc.

Bumble Inc. is an equal opportunity employer and we strongly encourage people of all ages, colour, 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).

As a Staff Machine Learning Engineer in the Recommendations group, you will be one of the most senior individual contributors shaping the technical direction and strategy behind how millions of people connect across Bumble Inc.’s apps. You will operate across multiple teams, driving the design, scaling and reliability of the systems that power our recommendation and content understanding models globally.

Your expertise will help evolve our ML architecture and platform, influence best practices across engineering and science, and ensure that Bumble’s machine learning systems are fast, robust, and responsible. You will work closely with ML Scientists, Data Engineers, Product Managers, and other engineering leaders to turn research into resilient production systems that deliver measurable impact for our members.

• Lead the technical strategy and architectural evolution of Bumble’s ML recommendation and content understanding systems.
• Partner with engineering and product leaders to align long‑term ML platform investments with business priorities and member impact.
• Design and guide the development of scalable pipelines and serving systems that support pre‑trained, fine‑tuned, and in‑house models at high throughput.
• Define and champion best practices for reliability, observability, and retraining across the ML lifecycle.
• Collaborate with ML Scientists to bring cutting‑edge research into production, improving model performance and iteration velocity.
• Mentor and support other Machine Learning Engineers and Scientists, helping raise the bar for engineering excellence and technical decision‑making.
• Drive cross‑functional technical initiatives across Recommendations, Platform, and other product areas.
• Diagnose and resolve complex production challenges across data, infrastructure, and model systems, ensuring the long‑term health and scalability of our ML ecosystem.
• Represent Bumble’s ML engineering practices internally (through guilds, design reviews, and architecture councils) and externally (through talks, publications, or open‑source contributions).

• Typically 8+ years of professional experience building and operating machine learning systems.
• An advanced degree in Computer Science, Mathematics or a similar quantitative discipline.
• Strong software engineering background. You write clean, scalable, and maintainable code in Python or similar languages.
• Deep expertise in building, deploying, and scaling production ML systems at large scale.
• Proven ability to define and lead technical strategy or architecture for complex, distributed ML platforms or pipelines.
• Experience with production‑grade ML frameworks (e.g. PyTorch, Tensor Flow) and orchestration tools (e.g. Airflow, Kubeflow, Ray, or Sage Maker).
• Proficiency with cloud‑native environments and containerised workloads (e.g. Docker, Kubernetes, GCP/AWS).
• Deep understanding of MLOps, observability, and model lifecycle management.
• Track record of mentoring engineers and influencing engineering practices across teams.
• Excellent communicator who can translate between technical detail and business impact.
• Passionate about responsible ML — fairness, transparency, and reliability in real‑world systems.

• Own meaningful projects that directly impact millions of Bumble users.
• Learn and grow in a high‑performing engineering team committed to mentorship and learning.
• Be part of a culture that values respect, excellence, curiosity, courage and joy.
• Enjoy competitive compensation, equity, and world‑class benefits.

• This role is based in Austin, and we ask that you’re within a commutable distance to this…