[Remote] Senior Machine Learning Engineer - Remote

Note: The job is a remote job and is open to candidates in USA. General Mills is a food company focused on creating products people love while fostering learning, growth, and innovation. The company is seeking a Senior Machine Learning Engineer to design, deploy, and support scalable AI and machine learning solutions in GCP, with responsibilities spanning software engineering, MLOps, production reliability, and emerging generative AI capabilities.

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 strong focus on reliability, supportability, and quality
• Partner across teams to understand problem definitions, data needs, and solution approaches for a wide range of business and technical use cases
• Prepare data, engineer features, develop and evaluate models, and help 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

Skills

• 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
• 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 the flexibility to match skills to evolving priorities

Benefits

• Remote work within the United States

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