Uber Sr Machine Learning Engineer Earner Growth 

United States, West Virginia 

77480289

What You'll Do

• Build statistical, optimization, and machine learning models
• Develop innovative new earner incentives that earners for choosing our network and optimizing Uber’s new earner incentives spend
• Optimize Uber’s background check spend and onboarding funnel
• Design recommendation engines to recommend the most relevant earning opportunities and early lifecycle content
• Develop matching algorithms for driver to driver mentorship program
• Model and predict earner behaviors to improve earner experience throughout the onboarding funnel
• The team employs a variety of ML/AI techniques, spanning from causal ML meta learners, supervised ML, RL multi-armed bandits, genAI LLM to deep learning embeddings to build impactful data products.
• Work closely with multi-functional leads to develop technical vision, new methodological approaches, and drive team direction.
• Collaborate with cross-functional teams such as product, engineering, operations, and marketing to drive ML system development end-to-end from conceptualization to final product.

Basic Qualifications

• PhD or equivalent experience in Computer Science, Machine Learning, Operations Research, Statistics, or other related quantitative fields or related field
• 4 years minimum of industry experience as a Machine Learning Engineer/Research Scientist with a strong focus on deep learning and probabilistic modeling.
• Proficiency in multiple object-oriented programming languages (e.g. Python, Go, Java, C++).
• Experience with any of the following: Spark, Hive, Kafka, Cassandra.
• Experience building and productionizing innovative end-to-end Machine Learning systems.
• Experience in exploratory data analysis, statistical modeling, hypothesis testing, and experimental design.
• Experience working with cross-functional teams(product, science, product ops etc).

Preferred Qualifications

• 5+ years of industry experience in machine learning, including building and deploying ML models.
• Publications at industry recognized ML conferences.
• Experience in modern deep learning architectures and probabilistic modeling.
• Experience with optimization techniques, including reinforcement learning (RL), Bayesian methods, causal ML meta learners, genAI LLM.
• Expertise in the design and architecture of ML systems and workflows.

For New York, NY-based roles: The base salary range for this role is USD$198,000 per year - USD$220,000 per year.

For San Francisco, CA-based roles: The base salary range for this role is USD$198,000 per year - USD$220,000 per year.

For Seattle, WA-based roles: The base salary range for this role is USD$198,000 per year - USD$220,000 per year.

For Sunnyvale, CA-based roles: The base salary range for this role is USD$198,000 per year - USD$220,000 per year.