Machine Learning Engineer

Key Skills

Agile methodologies, Continuous Integration & Continuous Delivery - CI/CD, Data pipelines, Data science techniques, Docker, ETL frameworks, Git, Google Cloud Platform (GCP), Keras software library, Machine learning techniques, MLOps tools, NumPy, Pandas, Python Programming, PyTorch, Scikit-learn, TensorFlow, XGBoost

Job Description

We strongly encourage people of colour, lesbian, gay, bisexual, transgender, queer and non-binary people, veterans, parents, and individuals with disabilities to apply. Bumble is an equal opportunity employer and welcomes everyone to our team. If you need reasonable adjustments at any point in the application or interview process, please let us know.

In your application, please feel free to note which pronouns you use (For example - she/her/hers, he/him/his, they/them/theirs, etc).

Bumble is looking for machine learning engineers to join our team and play a key role in fulfilling our mission to create a world where all relationships are healthy and equitable. Concretely, this means implementing, deploying and maintaining 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 to find love all over the world! The ideal candidate combines strong business acumen, extensive experience in machine learning applications along with a passion for tech.

WHAT YOU WILL BE DOING

• Work in a cross-functional team alongside data scientists and machine learning scientists
• Implement and deploy cutting-edge machine learning models for measurable impact across the business 
• Create the tools, frameworks and libraries that enable the acceleration of our ML product delivery
• Drive improvements to our current AI workflows in terms of process, performance and testing
• Mentor and coach team members on ML engineering best practices
• Build capabilities for our MLOps platform

WE’D LOVE TO MEET SOMEONE WITH

• An advanced degree in Computer Science, Mathematics or a similar quantitative discipline
• Hands-on experience in delivering machine learning models to production at scale
• Experience in writing production-quality Python code
• Comfortable working with Python data science and machine learning libraries such as scikit-learn, TensorFlow, Keras, pandas, numpy, PyTorch, XGBoost
• Strong understanding of machine learning applications development life cycle processes and tools: CI/CD, version control (git), testing frameworks, MLOps, agile methodologies, monitoring and alerting
• Comfortable working with Docker and containerised applications
• Experience accessing and combining data from multiple sources and building data pipelines (ETL)
• Ability to work collaboratively and proactively in a fast-paced environment alongside scientists, engineers, and non-technical stakeholders 
• Ability to combine business intuition with the application of advanced solutions
• A passion for keeping up with the latest ongoings in Data Science and Machine Learning communities
• A curious mind, self-starter and endlessly keen to learn and develop themselves professionally

AN ADDED BONUS IF YOU HAVE

• Hands-on experience with K8s
• Experience with cloud infrastructures - GCP is a plus
• Experience with On-call policies.