Machine Learning Engineer
Bumble Inc.
AirflowAWSCloudDockerGoogle Cloud PlatformKubernetesPythonPyTorchRayTensorflow
Staff Machine Learning Engineer
Bumble Inc.
full-time
Posted on:
Location Type: Hybrid
Location: Austin • Texas • 🇺🇸 United States
Visit company websiteSalary
💰 $240,000 - $292,500 per year
Job Level
Lead
Tech Stack
AirflowAWSCloudDockerGoogle Cloud PlatformKubernetesPythonPyTorchRayTensorflow
About the role
- 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).
Requirements
- 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.
Benefits
- 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.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
machine learning systemsPythonML frameworksPyTorchTensorFlowMLOpsmodel lifecycle managementscalable pipelinesproduction-grade systemsclean code
Soft skills
mentoringcommunicationtechnical strategycollaborationinfluencing practicesproblem-solvingleadershipengineering excellencecross-functional initiativesbusiness impact translation
Certifications
advanced degree in Computer Scienceadvanced degree in Mathematicsadvanced degree in quantitative discipline
