About the role

Staff Machine Learning Engineer in Recommendations shaping ML systems for Bumble. Collaborating across teams to design, scale, and ensure reliability in recommendation systems.

Responsibilities

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, TensorFlow) and orchestration tools (e.g. Airflow, Kubeflow, Ray, or SageMaker).
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.

Medical, Dental, Vision, 401(k) match, Unlimited Paid Time Off Policy.
Maven Fertility: $10,000 lifetime benefit for fertility, adoption, abortion care, and more.
26 Weeks Parental Leave: For both primary and secondary caregivers.
Family & Compassionate Leave: Inclusive of domestic violence recovery.
Unlimited Paid Time Off: Take the time you need.
Company-wide Week Off: Annual collective rest for the entire company.
Focus Fridays: No meetings, emails, or deadlines—just deep work.