About the job
Partners with stakeholders to design, develop, optimize, and productionize machine learning (ML) or ML-based solutions and systems that are used within a team to solve complex problems with multiple dependencies. This role also leads team efforts to leverage and improve ML infrastructure for model development, training, deployment needs and scaling ML systems.
Responsibilities
Design, build, and deploy scalable machine learning models to production to solve real-world business problems.
Collaborate with cross-engineering teams, data scientists and other partners to gather requirements and translate them into technical specification
Work closely with multi-functional leads to develop technical vision, new methodological approaches, and drive team direction.
Write clean, testable, and efficient code to ensure models run with low latency and high reliability.
Implement monitoring systems to track model performance, stability, and data drift in live environments.
Mentor and guide other engineers, providing technical leadership and encouraging a collaborative and growth-oriented team environment
Stay up-to-date with standard machine learning algorithms and industry trends to continuously improve our tech stack.
Qualifications
Minimum
Bachelor’s degree or equivalent in Machine Learning, AI, Data Science, Computer Science, Engineering, Mathematics or related field with at least 3 year of full-time Machine Learning work experience OR PhD in Machine Learning, AI, Data Science, Computer Science, Engineering, Mathematics or related field with at least 1 year of full-time Machine Learning work experience
Proficiency in at least one programming language such as Java, C++, Python, or Go
3 years of experience with ML algorithms/modeling- developing, training, productionization and monitoring of ML solutions at scale.
Preferred
Master’s degree or higher in Machine Learning, AI, Data Science, Computer Science, Engineering, Mathematics or related field.
More than 5 years of full-time machine learning work experience
Experience with the full ML lifecycle (at Uber Scale), including model deployment, containerization and workflow orchestration.
Experience in translating ambiguous business problems into technical solutions in a structured and principled way.
Strong communication skills, including through documentation and design discussions
Experience with optimization techniques and algorithmic development
Strong problem-solving skills, with expertise in algorithms, data structures, and complexity analysis
High bar for quality as demonstrated by code reviews, documentation, unit and integration testing