Machine Learning Engineer (NLP) - Trust & Safety

Key Skills

Algorithms, Continuous Integration & Continuous Delivery - CI/CD, Data science techniques, Employee life cycle, ETL frameworks, Google Cloud Platform (GCP), Infrastructure as code, Kubernetes-K8s, Large Language Models - LLMs, Machine learning techniques, MLOps tools, Natural Language Processing (NLP), NeurIPS

Job Description

Bumble is looking for a Machine Learning Engineer (NLP) to join our Trust & Safety team and play a key role in fulfilling our mission to create a world where all relationships are healthy and equitable. Concretely, we are looking for talents with a deep understanding of NLP algorithms (Transformers, LLMs, etc) and rich hands-on experience in building text-based ML systems. This means exploring, developing and deploying state of the art machine learning models that help Bumble provide a safe and engaging experience for our users and improve the way Bumble operates.

With millions of images and messages exchanged on our platform every day, there is a wealth of opportunity to make a real difference in this role and help people find love all over the world! The ideal candidate combines strong business acumen, extensive experience in machine learning applications (specifically in NLP), and a passion for tech.

WHAT YOU WILL BE DOING

• Work in a cross-functional team alongside data scientists, machine learning engineers, and both technical and non-technical stakeholders.
• Explore, develop and deliver new cutting-edge solutions for text-based ML, leveraging technologies like transformers, LLMs, etc.
• Set up and conduct large-scale experiments to test hypotheses and drive product development.
• Working with our MLOps Core K8s platform directly to efficiently serve models at a global scale.
• Deploy models, and lead their continuous monitoring & improvement.
• Keep up with state-of-the-art research, with the opportunity to create prototypes for the business and publish at top conferences.

WE’D LOVE TO MEET SOMEONE WITH

• An advanced degree in Computer Science, Mathematics or a similar quantitative discipline
• Experience in designing, developing, and training NLP models
• Experience with Kubernetes
• Understanding of software development life cycle processes and tools - ETL pipelines, CI/CD, testing frameworks
• Strong statistical modelling skills - hypotheses testing, inference, regressions, random variables, etc.
• The ability to work collaboratively alongside scientists, engineers and non-technical stakeholders
• Passion for keeping up with the latest ongoings in Data Science and Machine Learning communities
• A curious mind, who is a self-starter and endlessly keen to learn and develop themselves professionally

AN ADDED BONUS IF YOU HAVE

• An understanding of/experience with the Trust & Safety space
• Experience with cloud infrastructures - GCP is a plus
• Publications in top data science conferences like KDD, ECML, NeurIPS