Machine Learning Engineer

Job description

Your goal will be to improve user engagement by delivering the most relevant content to each user. This includes feed ranking (to recommend the best experiences on the main screen of the app), push notifications, contact ranking, friend suggestions. We're just at the beginning of our journey, so you'll have an opportunity to start from scratch with simple heuristics and models and gradually improve them as our user base grows.

You will work through the full lifecycle: collect the right data, explore it, extract insights and make hypotheses, perform feature engineering, train and evaluate models, launch A/B tests, make conclusions based on metrics (and propose and evaluate those metrics) and feedback, communicate them to stakeholders, deploy production services and integrate them with the rest of the backend, implement monitoring and alerting, fix live issues if necessary.

Qualifications

• Have good backend engineering skills. Fluent in at least one programming language. Comfortable working with Python.
• Have strong analytical skills and curiosity for exploring and making hypotheses. Can efficiently use SQL and Pandas to extract insights from data.
• Have a good grasp of ML fundamentals and lifecycle.
• Communicate clearly and effectively, including written form. Write clear proposals, seek alignment, request and act on feedback.
• Are proactive, keen to take ownership, can tolerate and resolve ambiguity. Have a pragmatic approach with a focus on improving user experience. Not intimidated by solving new types of problems and learning new tools.

Would be nice if you also:

• Have a track record of delivering full cycle ML projects, from data exploration, experimentation, to deploying and supporting models in production.
• Have experience working with modern MLOps practices and tools, such as MLFlow, Kubeflow, feature stores.
• Have experience of deploying and supporting backend services in production, working with Docker and Kubernetes.
• Comfortable working with Git.

Benefits

• Remote operation capability.
• Care bonus for medical insurance, fitness, education, etc.
• Flexible working hours.
• The project is connected with charity work, we are changing the world for the better.

Our interview process will cover

• Short Intro Talk with Recruiter about the role, expectations and other important details regarding the position.
• An interview with an engineer from our Personalization team. We will discuss your experience and interests, and you can ask questions about ranking and personalization in Locals. We will also discuss some ML principles and have a short coding session.
• An Interview with our Product team.