{"schemaVersion":"jobsearcher.job.v1","id":"6c62e5e85606e7f4aa93cd40","url":"https://jobsearcher.com/jobs/6c62e5e85606e7f4aa93cd40","canonicalUrl":"https://jobsearcher.com/jobs/6c62e5e85606e7f4aa93cd40","title":"Sr. Machine Learning Engineer, Earner Growth","description":"Sr. Machine Learning Engineer, Earner Growth\r\nUber\r\nFull-time\r\nAbout The Role\r\nHave 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?\r\nIf 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.\r\nWe 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. We work with millions of earners across the globe to make this magic happen and want you to join us!\r\nAbout The Team\r\nEarners (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.\r\nWithin 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.\r\nTherefore, 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 first is a great experience.\r\nWhat You'll Do\r\nBuild statistical, optimization, and machine learning models\r\nDevelop innovative new earner incentives that earners choose our network and optimize Uber's new earner incentives spend\r\nOptimize Uber's background check spend and onboarding funnel\r\nDesign recommendation engines to recommend the most relevant earning opportunities and early lifecycle content\r\nDevelop matching algorithms for driver to driver mentorship program\r\nModel and predict earner behaviors to improve earner experience throughout the onboarding funnel\r\nThe 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.\r\nWork closely with multi-functional leads to develop technical vision, new methodological approaches, and drive team direction.\r\nCollaborate with cross-functional teams such as product, engineering, operations, and marketing to drive ML system development end-to-end from conceptualization to final product.\r\nBasic Qualifications\r\nPhD or equivalent experience in Computer Science, Machine Learning, Operations Research, Statistics, or related quantitative fields.\r\n4 years minimum of industry experience as a Machine Learning Engineer/Research Scientist with a strong focus on deep learning and probabilistic modeling.\r\nProficiency in multiple object-oriented programming languages (e.g. Python, Go, Java, C++).\r\nExperience with any of the following: Spark, Hive, Kafka, Cassandra.\r\nExperience building and productionizing innovative end-to-end Machine Learning systems.\r\nExperience in exploratory data analysis, statistical modeling, hypothesis testing, and experimental design.\r\nExperience working with cross-functional teams (product, science, product ops, etc).\r\nPreferred Qualifications\r\n5+ years of industry experience in machine learning, including building and deploying ML models.\r\nPublications at industry-recognized ML conferences.\r\nExperience in modern deep learning architectures and probabilistic modeling.\r\nExperience with optimization techniques, including reinforcement learning (RL), Bayesian methods, causal ML meta learners, genAI LLM.\r\nExpertise in the design and architecture of ML systems and workflows.\r\nCompensation & Benefits\r\nFor New York, NY-based roles: The base salary range for this role is USD$202,000 per year – USD$224,000 per year. For SanFrancisco, CA-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. All full-time employees are eligible to participate in Uber's bonus program and may be offered an equity award and other types of compensation. 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 https://jobs.uber.com/en/benefits.\r\nJ-18808-Ljbffr","company":"SupportFinity","rawCompany":"supportfinity","city":"Millbrae","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-07-15T03:00:41.161Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"}],"industries":[{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"485310","title":"Taxi and Ridesharing Services","slug":"taxi-and-ridesharing-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Sr. Machine Learning Engineer, Earner Growth","description":"Sr. Machine Learning Engineer, Earner Growth\r\nUber\r\nFull-time\r\nAbout The Role\r\nHave 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?\r\nIf 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.\r\nWe 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. We work with millions of earners across the globe to make this magic happen and want you to join us!\r\nAbout The Team\r\nEarners (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.\r\nWithin 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.\r\nTherefore, 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 first is a great experience.\r\nWhat You'll Do\r\nBuild statistical, optimization, and machine learning models\r\nDevelop innovative new earner incentives that earners choose our network and optimize Uber's new earner incentives spend\r\nOptimize Uber's background check spend and onboarding funnel\r\nDesign recommendation engines to recommend the most relevant earning opportunities and early lifecycle content\r\nDevelop matching algorithms for driver to driver mentorship program\r\nModel and predict earner behaviors to improve earner experience throughout the onboarding funnel\r\nThe 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.\r\nWork closely with multi-functional leads to develop technical vision, new methodological approaches, and drive team direction.\r\nCollaborate with cross-functional teams such as product, engineering, operations, and marketing to drive ML system development end-to-end from conceptualization to final product.\r\nBasic Qualifications\r\nPhD or equivalent experience in Computer Science, Machine Learning, Operations Research, Statistics, or related quantitative fields.\r\n4 years minimum of industry experience as a Machine Learning Engineer/Research Scientist with a strong focus on deep learning and probabilistic modeling.\r\nProficiency in multiple object-oriented programming languages (e.g. Python, Go, Java, C++).\r\nExperience with any of the following: Spark, Hive, Kafka, Cassandra.\r\nExperience building and productionizing innovative end-to-end Machine Learning systems.\r\nExperience in exploratory data analysis, statistical modeling, hypothesis testing, and experimental design.\r\nExperience working with cross-functional teams (product, science, product ops, etc).\r\nPreferred Qualifications\r\n5+ years of industry experience in machine learning, including building and deploying ML models.\r\nPublications at industry-recognized ML conferences.\r\nExperience in modern deep learning architectures and probabilistic modeling.\r\nExperience with optimization techniques, including reinforcement learning (RL), Bayesian methods, causal ML meta learners, genAI LLM.\r\nExpertise in the design and architecture of ML systems and workflows.\r\nCompensation & Benefits\r\nFor New York, NY-based roles: The base salary range for this role is USD$202,000 per year – USD$224,000 per year. For SanFrancisco, CA-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. All full-time employees are eligible to participate in Uber's bonus program and may be offered an equity award and other types of compensation. 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 https://jobs.uber.com/en/benefits.\r\nJ-18808-Ljbffr","datePosted":"2026-07-15T03:00:41.161Z","dateModified":"2026-07-15T03:00:41.161Z","hiringOrganization":{"@type":"Organization","name":"SupportFinity","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Millbrae","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"6c62e5e85606e7f4aa93cd40"},"url":"https://jobsearcher.com/jobs/6c62e5e85606e7f4aa93cd40"}}