ARE YOU A CURRENT US FOODS EMPLOYEE? PLEASE APPLY DIRECTLY THROUGH OUR INTERNAL WORKDAY CAREER SITE. Join Our Community of Food People! The Sr. Machine Learning Engineer plays a crucial role in bridging the gap between the development and deployment of machine learning models while ensuring a smooth and efficient transition of functional AI models from the lab to real-world production environments. The focus of Sr. ML Engineer(s) is to build and automate ML CI/CD pipelines, containerize ML models, orchestrate infrastructure, and monitor and maintain the implementation by retraining and tracking metrics over time. This role independently owns at least one ML pipeline and has experience collaborating and engaging with various stakeholders. Sr. ML Engineer is also expected to mentor junior ML Engineers and assist in their interactions with various other stakeholders including Data Scientists, Site Reliability Engineers, domain experts, and other team members. This position is virtual/remote which means the work can be completed from anywhere in the United States except Hawaii or United States Territories. We are unable to provide sponsorship for employment visa status (e.g., OPT, F-1, H-1B visa status). Candidates who require sponsorship and are not eligible to work in the United States are not eligible to apply. This position is virtual/remote which means the work can be completed from anywhere in the United States except Hawaii or United States Territories. We are unable to provide sponsorship for employment visa status (e.g., OPT, F-1, H-1B visa status). Candidates who require sponsorship and are not eligible to work in the United States are not eligible to apply.
RESPONSIBILITIES As Sr ML Engineer, you will be expected to work independently across multiple projects on: Independently developing and deploying multiple pipelines and infrastructure around ML models / products. Independently maintaining the production ML models by retraining the models on new data, fixing bugs, and adding new features Independently troubleshooting machine learning problems by identifying the root cause of problems and developing solutions to fix them Following best practices in terms of coding and deployment of MLOps pipelines Independently providing production support to the technical team to manage/troubleshoot the failure of an application Documenting the production deployment with details regarding ML pipelines and associated. technical infrastructure Proactively contributing to coding review sessions and proactively seeking mastery of coding conventions Prototyping and creating POCs (proof of concepts) to demonstrate the feasibility of MLOps solutions Independently supporting scalable ML solutions that efficiently handle increasing data volumes, ensuring that the models remain effective and responsive as the business grows Independently supporting the integration of machine learning models into processes that require quick and automated responses Independently using and debugging robust monitoring systems for deployed models, promptly identifying, and addressing issues to ensure continuous reliability and performance including data and model drift Independently implementing machine learning features/solutions that enhance the overall user experience Mentoring Associate ML Engineers and ML Engineers in the context of technology, best practices, and domain-specific issues Other duties as assigned by manager RELATIONSHIPS Internal: ML Team (Associate ML Engineers, ML Engineers, Lead ML Engineers), SRE, Data Science team, Product Management team, CX/UX team. External: None. WORK ENVIRONMENT Remote: This role is fully remote, and the associate is expected to perform assigned responsibilities from a home-based environment. MINIMUM QUALIFICATIONS As an Sr. ML Engineer, you have: Bachelor’s degree plus 3+ years of relevant software development experience Effective written, verbal, and interpersonal communication skills, with the ability to work and communicate effectively with team members Advanced-level understanding of salient machine learning algorithms (for example: NLP, recommender systems) and model development lifecycle 2+ years’ experience with Agile/SCRUM methodologies 2+ years’ experience in AWS (or other clouds) relevant to machine learning including data processing & storage, API development, MLOps, CI/CD pipelines, and container orchestration (preferably ECS & EKS) 2+ years’ experience in using popular machine-learning frameworks 3+ years of practical experience (non-academic) in scripting languages (preferably Python and Bash scripting) 3+ years’ experience in data warehouses & data lakes and analytics services, such as Snowflake 3+ years’ experience in distributed computing concepts preferably (for example, AWS Elastic MapReduce) for processing large datasets 3+ years’ experience in version control systems/concepts, such as Git 3+ years’ experience in SQL and NoSQL 3+ years’ experience in orchestration tools like Airflow or Dagster 3+ years’ experience in working with structured, unstructured, and semi-structured data. 3+ years’ experience in serverless technologies 3+ years’ experience in cloud computing, Linux OS, Docker, RDBMS, and NoSQL Databases Up to 5% travel required, depending on business needs. The following information is provided in accordance with certain state and local laws. Compensation depends on experience, geographic locations, and other factors permitted by law. In Colorado, the expected compensation for this role is between $96,500.00 and $128,700.00. In New York, the expected compensation for this role is between $91,100.00 to $143,300.00. In California and Washington, the expected compensation for this role is between $102,100.00 and $136,200.00. This role is also eligible for an annual incentive plan bonus. Benefits for this role include health insurance, pre-tax spending accounts, retirement benefits, paid time off, short-term and long-term disability, employee stock purchase plan, and life insurance. To review available benefits, please click here: . #LI-EC1 #AC EOE* Race/Color/Religion/Sex/Sexual Orientation/Gender Identity/National Origin/Protected Veteran/Disability Status * Puede ver este sitio de empleo y aplicación en español utilizando la configuración de su navegador o teléfono móvil. Haga clic a continuación para obtener más información. Microsoft Edge ( Google Chrome Safari iPhone Androide ( US Foods is one of America’s great food companies and a leading foodservice distributor, partnering with approximately 300,000 restaurants and foodservice operators to help their businesses succeed. With 28,000 employees and more than 70 locations, US Foods provides its customers with a broad and innovative food offering and a comprehensive suite of e-commerce, technology and business solutions. US Foods is headquartered in Rosemont, Ill., and generates more than $28 billion in annual revenue. Visit to learn more. US Foods may collect personal information from you in connection with the application process. US Foods complies with the California Privacy Rights Act of 2020, and its policy may be found here ( . US Foods, Inc. is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other basis prohibited by applicable law. EEO is the Law poster is available here ( . EEO is the Law poster supplement is available here ( . Pay Transparency policy statement is available here ( . US Foods is committed to working with and providing reasonable accommodation to individuals with disabilities. If reasonable accommodation is needed to participate in the interview process or to perform essential job functions, please contact our US Foods Application Accommodation Line at . You will be prompted to leave a message. Please state the specifics of the assistance needed and your contact information. A member of our HR department will return your call within two business days.
Git CI/CD Data Science Python Amazon Web Services (AWS) Linux snowflake-cloud-data-platform amazon-ecs MLOps Docker amazon-eks SQL Airflow Machine Learning Bash Scrum RDBMS dagster NoSQL