Machine Learning Engineer designing and optimizing deep learning models for safety - critical environments at Destinus. Shaping the future of high - speed, autonomous flight technologies.
About the role
- Senior Machine Learning Engineer focused on scalability and productionisation at Bumble, building advanced ML models for actionable insights and recommendations. Collaborate with teams to ensure continuous performance and reliability of ML systems.
Responsibilities
- Design, build and optimise ML pipelines and production systems that train, evaluate and serve recommendation models efficiently and at scale.
- Work in a cross-functional team alongside data scientists, machine learning scientists, software engineers and both technical and non-technical stakeholders.
- Partner with ML Scientists to translate research models into efficient, maintainable, and well-tested production systems.
- Implement monitoring, observability, and retraining strategies to ensure continuous model performance in a dynamic, global environment.
- Contribute to the evolution of our ML infrastructure, including CI/CD, model registries, and feature stores.
- Diagnose and resolve production ML issues, such as data inconsistencies and model drift, to identify and resolve infrastructure bottlenecks.
- Champion engineering best practices for scalability, reliability, and reproducibility across the ML lifecycle.
Requirements
- 5+ years of relevant industry experience.
- 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.
- Proven experience deploying and operating ML systems in production environments.
- Deep understanding of MLOps and infrastructure concepts: CI/CD for ML, feature stores, model serving, observability, and versioning.
- Experience with modern ML frameworks (e.g. PyTorch, TensorFlow) and orchestration tools (e.g. Airflow, Kubeflow, SageMaker, Ray).
- Familiarity with containerisation and cloud-native environments (e.g. Docker, Kubernetes, GCP/AWS).
- Skilled at debugging complex, distributed ML systems and optimising for performance at scale.
- Excellent communicator and collaborator. You communicate effectively with scientists, engineers, and non-technical stakeholders.
- Interested in contributing to the responsible development of ML and AI, with a focus on building systems that are fair, equitable and accountable.
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.
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