About the Role
Software Engineer II – Machine Learning – building machine learning solutions for all risk and fraud applications at Uber.
Our team applies a variety of machine learning algorithms to solve the problems in the risk domain. We are on the lookout for individuals who demonstrate exceptional problem-solving skills, critical thinking, and a strong foundation in machine learning and coding skills. This role offers the opportunity to work across all levels of Uber’s ML stack, spanning from infrastructure to ML model development and productionization.
What the Candidate Will Need / Bonus Points – What the Candidate Will Do –
- Develop and productionize machine learning algorithms for Uber’s risk and fraud problems.
- Perform data analysis to understand and drive product insights, further model iterations.
- Continuously innovate and apply state-of-the-art ML algorithms at Uber Scale.
- Establish best practices and improve the rigor and bar of Applied ML.
– Basic Qualifications –
- Bachelor’s degree or higher in Computer Science, Engineering, Mathematics, or a related field, some software engineering experience gained through industry work.
- Proficiency in one or more object-oriented programming languages such as Python, Go, Java, C++.
- Experience with big-data architecture, ETL frameworks, and platforms (e.g., Hive, Spark, Presto)
- Working knowledge of contemporary machine learning and deep learning frameworks (e.g. PyTorch, TensorFlow, JAX).
– Preferred Qualifications –
- Deep understanding of all aspects of machine learning model lifecycles (from prototypes, feature engineering, training, inference, deployment, monitoring).
- Experiences with cutting-edge machine learning research (original research publications or experiences in applying or reproducing state-of-the-art methods).
- Experiences of building applications with large language models.
- Strong statistical and experimental foundation and acumen to develop insights from data.
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