Senior Software Engineer - Machine Learning

Uber

Amsterdam, Netherlands

You will lead projects and drive cross-team collaboration with engineers across various product teams, including UberEats, Rides, FinTech, etc. As a result of your work, our team will deliver impact across various products and business lines. If you are interested in building product facing systems, working on complex system problems, improving user experience, and helping drive Uber’s top-line metrics then Membership is the team for you.

What the Candidate Will Do

Design and build Machine Learning models with optimization engines;
Productionize and deploy these models for real-world application;
Collaborate with Product and cross-functional teams to brainstorm new solutions and iterate on the product;
Write high-quality code and uphold standards for testing and coverage;
Align the team on solutions to ambiguous problems and analyze the tradeoffs of different technical solutions;
Contribute to engineering cultivation in terms of quality, monitoring, and on-call practices;
Find opportunities to improve how our team operates and promote standard processes.

Basic Qualification

Bachelor’s degree or equivalent in Computer Science, Engineering, Mathematics or related field, with 5+ years of full-time engineering experience;
3 + years of ML experience and building ML models;
Experience working with multiple multi-functional teams(product, science, product ops etc.);
Expertise in one or more object-oriented programming languages (e.g. Python, Go, Java, C++);
Experience with big-data architecture, ETL frameworks and platforms;
Solid understanding of latest ML technologies, and libraries;
Proven track records of being a fast learner and go-getter, with willingness to get out of the comfort zone.

Preferred Qualifications

Experience with the design and architecture of ML systems and workflows;
Experience with building algorithmic solutions in production, making practical tradeoffs among algorithm sophistication, compute complexity, maintainability, and extensibility in production environments;
Experience with taking on vague business problems, translating them into ML + Optimization formulation, identifying the right features, model structure and optimization constraints, and delivering business impact;
Experience with optimizing Spark queries for better CPU and memory efficiency;
Experience owning and delivering a technically challenging, multi-quarter project end to end.

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