Senior Machine Learning Engineer at Bumble developing scalable AI systems for personalized user interactions. Leading machine learning model development and deployment from exploration to production.
About the role
- Lead Machine Learning Engineer at Bumble shaping user connections through machine learning. Driving end-to-end AI solutions while mentoring engineers in a hybrid work environment.
Responsibilities
- Lead the design and delivery of end-to-end ML systems powering recommendations, ranking, and personalization, driving measurable improvements in user outcomes
- Architect and optimise ML pipelines, integrating data processing, model training, evaluation, and deployment into scalable systems (e.g. Spark, Airflow)
- Build and deploy advanced models using modern frameworks (e.g. PyTorch), ensuring robustness in high-scale production environments
- Develop and apply LLM-based capabilities, including prompt design and fine-tuning approaches for production use cases
- Drive experimentation strategy, including A/B testing and offline evaluation, to continuously improve performance and inform product decisions
- Partner cross-functionally with Product, Engineering, and Data teams, collaborating with purpose to translate business challenges into ML solutions
- Mentor and support other engineers, fostering a culture of Curiosity, ownership, and continuous improvement
Requirements
- Typically requires 7–10 years of experience, though we welcome candidates with alternative backgrounds that demonstrate equivalent skills.
- Strong expertise in machine learning, with a track record of delivering end-to-end systems in production
- Proficiency in Python and ML frameworks (e.g. PyTorch, TensorFlow), with experience in areas such as recommendation systems, ranking, or NLP
- Experience designing and scaling ML pipelines and data systems (e.g. Spark, Airflow, or similar technologies)
- Hands-on experience with LLMs, including prompting, evaluation, and production integration
- Demonstrated ability to take ownership of ambiguous problems and deliver impactful outcomes
- Strong collaboration skills, working cross-functionally and taking ownership of shared goals
- Solid AI fluency, with the ability to guide best practices in model development and responsible AI usage within a team.
Job title
Job type
Experience level
Salary
Degree requirement
Tech skills
Location requirements
Designing and operating cloud - based MLOps capabilities supporting analytical and generative AI models. Collaborating with data science and business teams for high - impact AI solutions.
Machine Learning Engineer analyzing data structures and developing ML models for customer profiling in Azerbaijan. Collaborating on probabilistic modeling and data quality improvement.
Machine Learning Engineer at HackerRank working on integrity systems to improve model quality. Collaborating on strategies for new signals like audio analysis and behavioral anomalies.
Machine Learning Engineer developing integrity systems for assessing model quality at HackerRank. Collaborating on multimodal signal processing and improving model performance.
Architect designing enterprise - grade AI/ML architectures for Quantiphi. Leading AI applications and ML strategy with a focus on scalability, security, and integration.
Software Engineer for ML Infrastructure at Slack, architecting systems to support large scale AI deployment and reliability. Engage in deep systems engineering focusing on ML lifecycle and infrastructure scalability.
Machine Learning Engineer at Winnow developing AI solutions for food waste reduction. Collaborate with cross - functional teams and leverage cutting - edge technologies in food recognition.
Senior Engineer developing AI/ML solutions to enhance patient care at Edwards Lifesciences. Collaborating with cross - functional teams to deliver impactful technologies in healthcare.
Machine Learning Engineer designing and deploying machine learning models for DXC Technology. Collaborating with data scientists and optimizing solutions for impactful results.