Machine Learning Engineer at DentalMonitoring developing and improving ML models for orthodontic treatment solutions. Engage in deep learning and computer vision tasks within a hybrid work environment.
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.
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