Uber
Senior Engineering Manager - Machine Learning
Uber, San Francisco, California, United States, 94199
Senior Engineering Manager - Machine Learning
Join to apply for the
Senior Engineering Manager - Machine Learning
role at
Uber Senior Engineering Manager - Machine Learning
2 days ago Be among the first 25 applicants Join to apply for the
Senior Engineering Manager - Machine Learning
role at
Uber About The Role
We're looking for an experienced and visionary About The Role
We're looking for an experienced and visionary
Senior Machine Learning Manager
to lead the ML strategy and execution for Uber Grocery's
Catalog team
. This team powers
every single consumer-facing experience in the Uber Eats app
-from what users see when they open the app to the products they choose at checkout.
As the ML leader on this team, you'll be responsible for building and scaling a diverse range of AI/ML systems that make sense of vast, complex, and ever-evolving grocery data. This includes everything from
deep semantic understanding of catalog items
to
large-scale inventory forecasting
and
novel computer vision applications
that integrate directly with courier workflows. You'll guide a team of talented ML engineers and collaborate cross-functionally with product, design, operations, and platform teams to shape the future of grocery shopping on Uber Eats.
About The Team
The
Catalog team
sits at the heart of Uber Grocery. If you've ever searched for an item, scrolled through a carousel, or tapped on a product to see more info-
that's our work in action
. We provide the foundational intelligence that powers the Uber Eats grocery experience.
From an ML perspective, our scope is vast and challenging:
Catalog Understanding & Enrichment : We build models that determine what each item really is-its brand, flavor, color, and what kinds of customers might prefer it. Our enrichment models help transform raw merchant data into a delightful user experience. Product Relationships : Our systems learn how products relate to one another-what's a substitute, what's often bought together, and what combinations drive better outcomes for both customers and merchants. Inventory Forecasting : Grocery inventory is volatile and high-stakes. Our team builds and maintains large-scale ML forecasting systems to predict availability and reduce substitutions-at a scale few companies ever reach. Computer Vision for Real-World Mapping : We're pushing the frontier of real-time store mapping using computer vision. Couriers use their phone cameras to help digitize physical grocery stores, feeding our inventory systems with real-time data.
We're tackling some of the hardest, most high-impact problems in the grocery and ML space-at Uber scale. The team is rapidly growing with high impact and visibility from the top. We are responsible for developing state of the art technology to optimize the pricing and incentives strategy in our platform that directly drives efficiencies and effectiveness across user interactions with Uber.
Minimum Qualifications
PhD or equivalent in Computer Science, Engineering, Mathematics or related field AND 4-years full-time Software Engineering work experience OR 10-years full-time Software Engineering work experience, WHICH INCLUDES 4-years total technical software engineering experience in one or more of the following areas: Note the 4-years total of specialized software engineering experience may have been gained through education and full-time work experience, additional training, coursework, research, or similar (OR some combination of these). The years of specialized experience are not necessarily in addition to the years of Education & full-time work experience indicated. Programming language (e.g. C, C++, Java, Python, or Go) Large-scale training using data structures and algorithms Modern machine learning algorithms (e.g., tree-based techniques, supervised, deep, or probabilistic learning) Machine Learning Software such as Tensorflow/Pytorch, Caffe, Scikit-Learn, or Spark MLLib 4+ years of people management experience
Technical skills:
Required
Deep Learning Scalable ML architecture Experience in applying machine learning models to solve large-scale real-world problems
Preferred
Personalization, user understanding and targeting Optimization (RL/Bayes/Bandits) Causal inference
For New York, NY-based roles: The base salary range for this role is USD$257,000 per year - USD$285,500 per year. For San Francisco, CA-based roles: The base salary range for this role is USD$257,000 per year - USD$285,500 per year. For Sunnyvale, CA-based roles: The base salary range for this role is USD$257,000 per year - USD$285,500 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. You will also be eligible for various benefits. More details can be found at the following link https://www.uber.com/careers/benefits. Seniority level
Seniority level Mid-Senior level Employment type
Employment type Full-time Job function
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Join to apply for the
Senior Engineering Manager - Machine Learning
role at
Uber Senior Engineering Manager - Machine Learning
2 days ago Be among the first 25 applicants Join to apply for the
Senior Engineering Manager - Machine Learning
role at
Uber About The Role
We're looking for an experienced and visionary About The Role
We're looking for an experienced and visionary
Senior Machine Learning Manager
to lead the ML strategy and execution for Uber Grocery's
Catalog team
. This team powers
every single consumer-facing experience in the Uber Eats app
-from what users see when they open the app to the products they choose at checkout.
As the ML leader on this team, you'll be responsible for building and scaling a diverse range of AI/ML systems that make sense of vast, complex, and ever-evolving grocery data. This includes everything from
deep semantic understanding of catalog items
to
large-scale inventory forecasting
and
novel computer vision applications
that integrate directly with courier workflows. You'll guide a team of talented ML engineers and collaborate cross-functionally with product, design, operations, and platform teams to shape the future of grocery shopping on Uber Eats.
About The Team
The
Catalog team
sits at the heart of Uber Grocery. If you've ever searched for an item, scrolled through a carousel, or tapped on a product to see more info-
that's our work in action
. We provide the foundational intelligence that powers the Uber Eats grocery experience.
From an ML perspective, our scope is vast and challenging:
Catalog Understanding & Enrichment : We build models that determine what each item really is-its brand, flavor, color, and what kinds of customers might prefer it. Our enrichment models help transform raw merchant data into a delightful user experience. Product Relationships : Our systems learn how products relate to one another-what's a substitute, what's often bought together, and what combinations drive better outcomes for both customers and merchants. Inventory Forecasting : Grocery inventory is volatile and high-stakes. Our team builds and maintains large-scale ML forecasting systems to predict availability and reduce substitutions-at a scale few companies ever reach. Computer Vision for Real-World Mapping : We're pushing the frontier of real-time store mapping using computer vision. Couriers use their phone cameras to help digitize physical grocery stores, feeding our inventory systems with real-time data.
We're tackling some of the hardest, most high-impact problems in the grocery and ML space-at Uber scale. The team is rapidly growing with high impact and visibility from the top. We are responsible for developing state of the art technology to optimize the pricing and incentives strategy in our platform that directly drives efficiencies and effectiveness across user interactions with Uber.
Minimum Qualifications
PhD or equivalent in Computer Science, Engineering, Mathematics or related field AND 4-years full-time Software Engineering work experience OR 10-years full-time Software Engineering work experience, WHICH INCLUDES 4-years total technical software engineering experience in one or more of the following areas: Note the 4-years total of specialized software engineering experience may have been gained through education and full-time work experience, additional training, coursework, research, or similar (OR some combination of these). The years of specialized experience are not necessarily in addition to the years of Education & full-time work experience indicated. Programming language (e.g. C, C++, Java, Python, or Go) Large-scale training using data structures and algorithms Modern machine learning algorithms (e.g., tree-based techniques, supervised, deep, or probabilistic learning) Machine Learning Software such as Tensorflow/Pytorch, Caffe, Scikit-Learn, or Spark MLLib 4+ years of people management experience
Technical skills:
Required
Deep Learning Scalable ML architecture Experience in applying machine learning models to solve large-scale real-world problems
Preferred
Personalization, user understanding and targeting Optimization (RL/Bayes/Bandits) Causal inference
For New York, NY-based roles: The base salary range for this role is USD$257,000 per year - USD$285,500 per year. For San Francisco, CA-based roles: The base salary range for this role is USD$257,000 per year - USD$285,500 per year. For Sunnyvale, CA-based roles: The base salary range for this role is USD$257,000 per year - USD$285,500 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. You will also be eligible for various benefits. More details can be found at the following link https://www.uber.com/careers/benefits. Seniority level
Seniority level Mid-Senior level Employment type
Employment type Full-time Job function
Job function Engineering and Information Technology Industries Internet Marketplace Platforms Referrals increase your chances of interviewing at Uber by 2x Get notified about new Manager of Machine Learning jobs in
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Redwood City, CA $225,000.00-$260,000.00 1 week ago We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.
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