Lead Machine Learning Engineer - US Remote Eligible

General Mills is looking for a Lead Machine Learning Engineer. In this role, you are a technical thought leader within the data science group focused on leading efforts in operationalizing machine learning based solutions from concept to production-level operational excellence. You will lead initiatives building scalable, resilient, and automated solutions in GCP (Google Cloud Platform) to ensure that models deliver on organizational objectives. You will professionally engineer solutions taking into account notions of risk and FMEA (failure modes and effects analysis). ACCOUNTABILITIES: As a technical thought leader, you will be working as a Lead ML Engineer to operationalize key strategic ML models. Leads complex ML and dev ops projects/programs involving teams of resources spanning the globe including full accountability for stakeholder management and overall project success while adhering to timeline/resource constraints. Investigate and assess novel machine learning and ML Ops approaches far ahead of operational feasibility. Create proposed technology roadmaps incorporating anticipated industry and academic directions. Regularly writing/publishing on well-known industry blogs and professional publications. Research and operationalize technology and processes necessary to scale ML models. Recommend model changes to optimize cloud spend. Automate monitoring of models both for failures and degradation. Automate monitoring of data sources to identify issues and/or data changes. Automate ML pipelines. Improve ML pipeline documentation and understandability. Automate logging of model usage and predictions provided. Harden code and processes to reduce or prevent failures. Improve logging and diagnostic processes. Lead the investigation and resolution of production issues, perform root cause analysis, and recommend changes to reduce/eliminate re-occurrence of issues. Optimize deployment and change control processes for models. Create and operationalize quality assurance processes for ML models. Creating and curating a reusable data and feature store for use across models. QUALIFICATIONS: Advanced degree in a quantitative field (CS, engineering, statistics, math, data science). Proven ability to successfully execute on complex ML and dev ops programs involving multiple resources ideally including global teams. Strong knowledge of project/program management terminology/concepts and the ability to leverage this knowledge to ensure the success of initiatives. Proven ability to stay ahead of rapidly evolving technology - anticipating and influencing adoption beyond GMI. Proven technical leadership in a large, complex matrixed organization. 8+ years professional experience as a software engineer or data scientist. Strong background in statistical modeling including ARIMA, regression, and Random Forest models. At least 2 years professional experience with a major cloud computing platform such as GCP, Azure, Snowflake, or Data Bricks. Demonstrated ability to create elegant, re-usable ML libraries in Python using classic and novel design patterns. Ability to create/design code which is extensible at multiple levels with an intuition to identify abstractions which add value versus those which needlessly complicate. Demonstrated experience as a thought leader and mentor in the art and science of well-engineered code in general and in Python in particular. Track record of producing machine learning models and production infrastructure at scale. Strong verbal and written communication skills including the ability to interact effectively with colleagues of varying technical and non-technical abilities. Passionate about agile software processes, data-driven development, reliability, and systematic experimentation. Passion for learning new technologies and solving challenging problems. Good understanding of CI, CD, TDD and tools such as Jenkins. Strong understanding of orchestration frameworks such as Prefect or Airflow. Experience with Kubeflow, MLFlow, or GCP Vertex AI Pipelines strongly preferred. Solid understanding of dbt. Agile software development experience such as Kanban and Scrum. Experience in software version control team practices and tools such as GIT and TFS. Proven ability to mentor others and lead the team in technology and best practices. Proven ability to critically review and edit draft technical publications from others. COMPANY OVERVIEW We exist to make food the world loves. But we do more than that. Our company is a place that prioritizes being a force for good, a place to expand learning, explore new perspectives and reimagine new possibilities, every day. We look for people who want to bring their best — bold thinkers with big hearts who challenge one other and grow together. Because becoming the undisputed leader in food means surrounding ourselves with people who are hungry for what’s next.
General Mills is looking for a Lead Machine Learning Engineer. In this role, you are a technical thought leader within the data science group focused on leading efforts in operationalizing machine learning based solutions from concept to production-level operational excellence. You will lead initiatives building scalable, resilient, and automated solutions in GCP (Google Cloud Platform) to ensure that models deliver on organizational objectives. You will professionally engineer solutions taking into account notions of risk and FMEA (failure modes and effects analysis). ACCOUNTABILITIES: As a technical thought leader, you will be working as a Lead ML Engineer to operationalize key strategic ML models. Leads complex ML and dev ops projects/programs involving teams of resources spanning the globe including full accountability for stakeholder management and overall project success while adhering to timeline/resource constraints. Investigate and assess novel machine learning and ML Ops approaches far ahead of operational feasibility. Create proposed technology roadmaps incorporating anticipated industry and academic directions. Regularly writing/publishing on well-known industry blogs and professional publications. Research and operationalize technology and processes necessary to scale ML models. Recommend model changes to optimize cloud spend. Automate monitoring of models both for failures and degradation. Automate monitoring of data sources to identify issues and/or data changes. Automate ML pipelines. Improve ML pipeline documentation and understandability. Automate logging of model usage and predictions provided. Harden code and processes to reduce or prevent failures. Improve logging and diagnostic processes. Lead the investigation and resolution of production issues, perform root cause analysis, and recommend changes to reduce/eliminate re-occurrence of issues. Optimize deployment and change control processes for models. Create and operationalize quality assurance processes for ML models. Creating and curating a reusable data and feature store for use across models. QUALIFICATIONS: Advanced degree in a quantitative field (CS, engineering, statistics, math, data science). Proven ability to successfully execute on complex ML and dev ops programs involving multiple resources ideally including global teams. Strong knowledge of project/program management terminology/concepts and the ability to leverage this knowledge to ensure the success of initiatives. Proven ability to stay ahead of rapidly evolving technology - anticipating and influencing adoption beyond GMI. Proven technical leadership in a large, complex matrixed organization. 8+ years professional experience as a software engineer or data scientist. Strong background in statistical modeling including ARIMA, regression, and Random Forest models. At least 2 years professional experience with a major cloud computing platform such as GCP, Azure, Snowflake, or Data Bricks. Demonstrated ability to create elegant, re-usable ML libraries in Python using classic and novel design patterns. Ability to create/design code which is extensible at multiple levels with an intuition to identify abstractions which add value versus those which needlessly complicate. Demonstrated experience as a thought leader and mentor in the art and science of well-engineered code in general and in Python in particular. Track record of producing machine learning models and production infrastructure at scale. Strong verbal and written communication skills including the ability to interact effectively with colleagues of varying technical and non-technical abilities. Passionate about agile software processes, data-driven development, reliability, and systematic experimentation. Passion for learning new technologies and solving challenging problems. Good understanding of CI, CD, TDD and tools such as Jenkins. Strong understanding of orchestration frameworks such as Prefect or Airflow. Experience with Kubeflow, MLFlow, or GCP Vertex AI Pipelines strongly preferred. Solid understanding of dbt. Agile software development experience such as Kanban and Scrum. Experience in software version control team practices and tools such as GIT and TFS. Proven ability to mentor others and lead the team in technology and best practices. Proven ability to critically review and edit draft technical publications from others.