Software Engineer, Machine Learning
- London, United Kingdom
Facebook's mission is to give people the power to build community and bring the world closer together. Through our family of apps and services, we're building a different kind of company that connects billions of people around the world, gives them ways to share what matters most to them, and helps bring people closer together. Whether we're creating new products or helping a small business expand its reach, people at Facebook are builders at heart. Our global teams are constantly iterating, solving problems, and working together to empower people around the world to build community and connect in meaningful ways. Together, we can help people build stronger communities - we're just getting started.
Facebook is seeking Lead/Principal machine learning engineers to join our engineering team. The ideal person will have industry experience working on a range of classification and optimisation problems, e.g. payment fraud, click-through rate prediction, recommendation systems, click-fraud detection, search ranking, text/sentiment classification or spam detection. The position will involve taking these skills and applying them to some of the most exciting and massive social data and prediction problems that exist on the web. You will bring the ability to own the whole ML life cycle, define projects and drive excellence across teams.
- Play a critical role in setting the direction and goals for a sizable team, in terms of project impact, ML system design, and ML excellence
- Be a go-to person to escalate the most complex online / production performance and evaluation issues, that require an in depth knowledge of how the machine learning system interacts with systems around it
- Develop highly scalable classifiers and tools leveraging machine learning, data regression, and rules based models
- Suggest, collect and synthesise requirements and create effective feature roadmap
- Code deliverables in tandem with the engineering team
- Adapt standard machine learning methods to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU)
- Re-evaluate the tradeoffs of already shipped features/ML systems, and you are able to drive large efforts across multiple teams to reduce technical debt, designing from first principles when appropriate.
- Experience in one or more of the following areas: machine learning, recommendation systems, pattern recognition, data mining or artificial intelligence
- Proven ability to translate insights into business recommendations
- Experience with Hadoop/HBase/Pig or MapReduce/Sawzall/Bigtable/Hive/Spark
- Expert knowledge developing production level ML products
- Expert with scripting languages such as Python, Perl, PHP, and/or shell scripts Experience developing and debugging in Java, C++ or similar
- Experience of demonstrating technical leadership working with teams, owning projects, defining and setting technical direction for projects.
- MS degree in Computer Science or related quantitative field with experience in machine learning related work or research, or PhD degree in Computer Science or related quantitative field
- Experience with filesystems, server architectures and distributed systems
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