Staff Machine Learning Engineer

About the RoleWe are looking for a highly motivated Staff Machine Learning Engineer to join the Uber Eats Search and Discovery Team. You will play a critical role in enhancing the search experience for millions of Uber Eats users worldwide. You will leverage your expertise in data analysis, machine learning, and Engineering to drive insights and optimize search algorithms, ultimately improving user satisfaction and operational efficiency.\-\-\-\- What the Candidate Will Do ----- Lead the design, development, optimization, and productionization of machine learning (ML) solutions and systems to solve strategically important and/or vaguely defined problems in the field of Search and Discovery, GenAI , Query Understanding ( In delivery search space)- Design and analyze experiments using a combination of data analysis/statistical analysis to lead the team to a reasonable inference.- Collaborate with Product and cross-functional teams to brainstorm new solutions and iterate on the product.- Provide technical leadership and direction to fellow software & ML engineers in the team\-\-\-\- Basic Qualifications ----- PhD or M.S. experience in Computer Science, Engineering, Mathematics or a related field and 7+ years of ML engineering industry experience.- Experience in developing, training, productionizing and monitoring of ML/DL solutions at scale using packages such as Tensorflow, PyTorch, JAX, and Scikit-Learn.- Experience building ETL and data pipelines using Spark, Hive, HDFS or related technologies.- Solid understanding of statistical analysis and feature engineering techniques.- Excellent communication and collaboration skills.- Ability to work independently and take ownership of projects.- Experience using SQL in a production environment.- Experience in experimental design and analysis, exploratory data analysis, and statistical analysis.- Experience with dashboarding and using data visualization tools.- Experience using statistical methodologies such as sampling, statistical estimates, descriptive statistics, or similar.- Experience in programming with modern languages such as Python, Java, or Go.\-\-\-\- Preferred Qualifications ----- Experience developing Search and Recommendation systems at scale.- Well versed in translating open ended problem statements into well defined ML design- Experience working with large-scale distributed systems.- Experience working as a Technical lead for a small team of engineers.For San Francisco, CA-based roles: The base salary range for this role is USD$218,000 per year - USD$242,000 per year.For Seattle, WA-based roles: The base salary range for this role is USD$218,000 per year - USD$242,000 per year.For Sunnyvale, CA-based roles: The base salary range for this role is USD$218,000 per year - USD$242,000 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. You will also be eligible for various benefits. More details can be found at the following link [https://www.uber.com/careers/benefits](https://www.uber.com/careers/benefits).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 [this form](https://forms.gle/aDWTk9k6xtMU25Y5A).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.