Senior Machine Learning Engineer - Remote

We exist to make food the world loves. Our company prioritizes being a force for good, expanding learning, exploring new perspectives, and reimagining possibilities. We look for bold thinkers with big hearts who challenge one another and grow together to become the undisputed leader in food.

About the role

General Mills is seeking a Senior Machine Learning Engineer to design, build, deploy, and support scalable AI and machine learning solutions that deliver real business value. This role sits within a broader shared team model, where talent is matched to high-priority work across a growing portfolio of AI initiatives. You may work on traditional machine learning, foundational platform capabilities, or newer agentic and generative AI use cases depending on team needs and your strengths. We are looking for a strong technical engineer with solid software engineering discipline, Python skills, and flexibility to work across evolving AI problem spaces.

Responsibilities

• Design, develop, deploy, and maintain machine learning and AI systems in GCP to solve complex business problems, improve operations, and create new value.
• Translate machine learning concepts into practical, scalable production solutions with a focus on reliability, supportability, and quality.
• Partner across teams to understand problem definitions, data needs, and solution approaches for business and technical use cases.
• Prepare data, engineer features, develop and evaluate models, and operationalize solutions in production environments.
• Build and automate ML pipelines, including orchestration, monitoring, logging, diagnostics, and alerting for failures, drift, degradation, and upstream data issues.
• Support model deployment, MLOps practices, cloud resource management, and change control processes.
• Research, evaluate, and operationalize new tools, frameworks, platforms, and processes that help scale AI solutions, including emerging agentic and generative AI capabilities.
• Take ownership of production issues, perform root cause analysis, and drive improvements to reduce repeat incidents.
• Create and improve documentation, standards, and quality assurance processes for machine learning systems and pipelines.
• Help create, maintain, and support production and lower environments, including development, QA, and staging.
• Contribute to cloud security and compliance practices.
• Mentor others and help raise the team’s engineering and machine learning best practices.

Requirements

• Bachelor’s degree in computer science, engineering, statistics, mathematics, data science, or another quantitative field.
• 3+ years of professional experience as a software engineer, integration engineer, ML engineer, AI engineer, or data scientist.
• 3+ years of professional experience working with a major cloud platform such as GCP, Azure, Snowflake, or Databricks.
• Strong Python development skills.
• Experience building, deploying, or supporting production-grade machine learning or AI solutions.
• Familiarity with CI/CD, TDD, and related engineering tools and practices.
• Experience with orchestration frameworks such as Prefect or Airflow.
• Experience working in agile software development environments such as Kanban or Scrum.
• Experience with version control and team-based development practices using tools such as Git or TFS.
• Strong verbal and written communication skills, with the ability to work effectively with both technical and non-technical partners.
• Passion for learning new technologies, solving challenging problems, and operating with a data-driven engineering mindset.

Preferred Qualifications

• 5+ years of professional experience as a software engineer, integration engineer, ML engineer, AI engineer, or data scientist.
• Strong software engineering background, ideally including several years of hands-on engineering experience before or alongside machine learning work.
• Experience in a GCP environment, including Vertex AI.
• Experience building and supporting APIs and endpoints, ideally in GCP.
• Experience building, maintaining, and supporting traditional machine learning pipelines in a cloud environment.
• Background in statistical modeling techniques such as regression, ARIMA, Random Forest, optimization, or forecasting.
• Exposure to agentic AI platforms, generative AI solutions, or related modern AI tooling.
• Track record of producing machine learning models and production infrastructure at scale.
• Ability to mentor others and lead through engineering and ML best practices.
• Experience working across a variety of use cases or business domains, with flexibility to match skills to evolving priorities.

This role is open to remote employees within the United States. International relocation or remote arrangements outside the U.S. will not be considered. Applicants must be currently authorized to work in the United States on a full-time basis without visa sponsorship.