Sr. Machine Learning Engineer, Earner Growth - Sunnyvale
2 days ago
Job description
About The RoleHave you ever ordered a car service on Uber, and when the ride arrives, wondered how it got to you so fast? Ever ordered food on UberEats and wondered where the driver was before receiving your order and how long it took to get to the restaurant or if your order was ready when the courier arrived? Ever wondered why your grocery delivery from Uber always has the best apple picked?
If so, Uber is for you. In our ML and Science division, we strive to make magic within Uber's marketplace.
This requires judgment to make difficult trade-offs, blending algorithms with human resourcefulness, and the ability to build simplicity from complexity.
When we get the balance right for everyone, Uber magic happens. We build systems to peer into the future to craft the most cost-efficient marketplace for matching supply and demand.We are passionate about using innovative economics, machine learning, and scalable distributed software that automates and optimizes every aspect of this intricate dance between participants of the marketplace.
We are involved in every stage of the product development cycle and use data to inform product decisions, build models to power our solutions, and also develop platform tools that are used across teams with a primary focus on Mobility and Delivery.
About The Team
Earners (drivers and couriers) are an integral part of Uber's multi-sided marketplace. They provide the time and the means to move people and things.
Importantly, they enable the connection between the physical and digital world to make the movement happen at the push of a button for everyone, everywhere.
Within Uber, Earner Growth plays a critical role in earners' journey as the team is responsible for earner onboarding, activation, early life cycle, and resurrection.
This presents the teams with the opportunity to shape and tailor the product experience during earners' many firsts (i.e., first time interacting on the Uber platform, choosing the earning opportunity, going online, receiving incentive offers, completing a trip, or reading the earnings summary).
These firsts can be daunting.
Therefore, making sure that the earner journey is great at every touch point is important to build trust with Earners, communicate Uber's value proposition, and ensure each firsts to be a great experience.
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 QualificationsPhD 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.
Uber's mission is to reimagine the way the world moves for the better. Here, bold ideas create real-world impact, challenges drive growth, and speed fuelds progress. What moves us, moves the world - let's move it forward, together.
Offices continue to be central to collaboration and Uber's cultural identity.
Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office.
For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time.
Please speak with your recruiter to better understand in-office expectations for this role.Accommodations may be available based on religious and/or medical conditions, or as required by applicable law. To request an accommodation, please reach out to .
For New York, NY-based roles: The base salary range for this role is USD$202,000 per year - USD$224,000 per year.
For Seattle, WA-based roles:
The base salary range for this role is USD$202,000 per year - USD$224,000 per year
For Sunnyvale, CA-based roles:
The base salary range for this role is USD$202,000 per year - USD$224,000 per year.
For all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp.
All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits. More details can be found at the following linkSimilar jobs
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