ML Engineer

Industry: Media & Internet
Company size: 251-10K

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Job description

A globally popular dating social network that is on a mission to create a world where all relationships are healthy and equitable, is looking for a ML Engineer. The engineer will be responsible for implementing, deploying, and maintaining state-of-the-art machine learning models that help provide a safe and engaging experience for users and improve the way the company operates. The company enables users to exchange millions of images and messages on the platform every day. There is a wealth of opportunities 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 along with a passion for tech.

Job Responsibilities:

  • 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

Job Requirements:

  • Bachelor’s/Master’s degree in Engineering, Computer Science, Mathematics or a similar quantitative discipline
  • At least 3+ years of relevant experience as a software engineer
  • Hands on experience in delivering machine learning models to production at scale
  • Prior experience in writing production-quality Python code
  • Comfortable working with Python data science and machine learning libraries with a particular focus on deep learning frameworks 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
  • Operating and supporting Kubernetes clusters at scale
  • Nice to have some knowledge of DevOps and Google Cloud Platform (GCP)
  • 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
  • Fluent in spoken and written English communication

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