Uber AI's mission is to optimize and innovate Uber's products and business using machine learning and AI. The group consists of Uber's machi...
Uber AI's mission is to optimize and innovate Uber's products and business using machine learning and AI. The group consists of Uber's machine learning platform team which enables machine learning at scale, AI building blocks which enable product teams to build unique experiences and engagements with product teams on their business problems.
The group consists of machine learning engineers, mobile engineers, backend engineers and research scientists and engineers.
About the Role
Uber AI Engagements collaborates with partner teams across Uber to deliver innovative ML/AI solutions for core business problems. You will work closely with engineering, product and data science teams to understand business problems and the potential for ML/AI solutions. You will deliver these solutions from inception to production. Machine Learning Engineers have deep domain knowledge in ML/AI and the ability to apply that knowledge to diverse problem domains involving multiple stakeholders.
What You'll Do:
- Develop innovative ML/AI solutions for challenging business problems that are fundamental for Uber.
- Partner with Product teams to analyze key business problems.
- Deliver enduring value in terms of software and modeling artifacts.
- Collaborate with product managers, data scientists, and backend engineers on partner teams to integrate and validate ML solutions end to end.
- Masters in computer science or a closely related field with a concentration in machine learning, or equivalent experience
- 2+ years of industry experience in Applied Machine Learning
- Proficiency in Python and experience with ML frameworks, such as PyTorch and TensorFlow.
- Ability to work with ambiguous problem definitions.
- Ability to communicate technical knowledge to a business audience.
- Experience working with product teams.
- Expertise in knowledge graphs and/or recommendation systems.
- Experience with one or more of Spark, Hive, Kafka, Cassandra.
- Proven ability to innovate, as demonstrated by a track record of software artifacts or publications.
- Proven ability to deliver end-to-end solutions, including data preparation, training, and production deployment.
- Collaborative attitude and constructive mindset.
- provided by Dice