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Sr. Machine Learning Engineer
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About the Role
Contributes to the design, development, and optimization of machine learning solutions and systems for content classification, retrieval, and ranking. This role also learns to use and improve ML infrastructure for model development, training, and deployment.
ML Foundations in Uber Eats is deeply engaged in foundational work that impacts many products within the organization. Our team develops key modeling artifacts critical for our business, including entity classifications, entity resolution, attribute enrichments, semantic similarity and complementary recommendation models, and user profiles. We adopt cutting-edge, robust machine learning building blocks for Uber Eats .
We are on the lookout for individuals who demonstrate exceptional problem-solving skills, critical thinking, and a strong foundation in coding. Ideal This role offers the opportunity to work across all levels of the ML stack, spanning from infrastructure to ML model development and production.
---- What the Candidate Will Do ----
For Sunnyvale, CA-based roles: The base salary range for this role is USD$185,000 per year - USD$205,500 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 .
Uber is proud to be an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing .
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.
Contributes to the design, development, and optimization of machine learning solutions and systems for content classification, retrieval, and ranking. This role also learns to use and improve ML infrastructure for model development, training, and deployment.
ML Foundations in Uber Eats is deeply engaged in foundational work that impacts many products within the organization. Our team develops key modeling artifacts critical for our business, including entity classifications, entity resolution, attribute enrichments, semantic similarity and complementary recommendation models, and user profiles. We adopt cutting-edge, robust machine learning building blocks for Uber Eats .
We are on the lookout for individuals who demonstrate exceptional problem-solving skills, critical thinking, and a strong foundation in coding. Ideal This role offers the opportunity to work across all levels of the ML stack, spanning from infrastructure to ML model development and production.
---- What the Candidate Will Do ----
- Develop and productionize machine learning algorithms for multiple business problems
- Perform data analysis to understand and drive product insights, further model iterations.
- Continuously innovate and apply state-of-the-art ML algorithms at Uber Scale.
- Establish best practices and improve the rigor and bar of ML in Uber Eats
- Completing a Bachelor's degree or equivalent in Computer Science, Engineering, Mathematics, or a related field, plus a 5+ years of total software engineering experience gained through industry work.
- Proficiency in one or more object-oriented programming languages such as Python, Go, Java, C++.
- Experience with big-data architecture, ETL frameworks, and platforms (e.g., Hive, Spark, Presto)
- Working knowledge of contemporary machine learning and deep learning frameworks (e.g. PyTorch, TensorFlow, JAX).
- Multimodal Classification (Natural Language Processing, Computer Vision)
- Experience building reusable embeddings, applications and fine tuning of large language models.
- Deep understanding of all aspects of machine learning model lifecycles (from prototypes, feature engineering, training, inference, deployment, monitoring).
- Strong statistical and experimental foundation and acumen to develop insights from data.
For Sunnyvale, CA-based roles: The base salary range for this role is USD$185,000 per year - USD$205,500 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 .
Uber is proud to be an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing .
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.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
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