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About This Role
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 another and grow together. Because becoming the undisputed leader in food means surrounding ourselves with people who are hungry for what’s next.
JOB OVERVIEW:
General Mills is looking for a Lead ML Engineer. In this role, you are a technical lead within the AIA team focused on delivering the migration of machine learning\-based solutions and agents from concept to production \-level operational excellence . You will deliver initiatives such as building scalable, resilient, and automated solutions in GCP , Glean, UnifyApps, and other platforms to ensure that agents and models deliver on organizational objectives. You will professionally engineer solutions, taking into account notions of risk and effective/efficient use of computational resources.
KEY ACCOUNTABILITIES:
As a Lead ML Engineer, you will play a pivotal role in the overall success of high\-priority projects including:
- Lead ing complex machine learning and agentic projects involving multiple resources including full accountability for stakeholder management and expectation setting , project scoping and planning, requirements elicitation, and overall project success while adhering to timeline/resource constraints. Crucially, having the ability to provide stakeholders with a range of options for solving a stated business problem is needed.
- Playing a key role in investigating, utilizing, and advocating for the use of generative and agentic tools to enhance the effectiveness of your team .
- Ability to effectively integrate an AI solution into a larger, complex system potentially comprising many third\-party applications within the General Mills ecosystem.
- Research and operationalize technology and processes necessary to scale ML models and agentic solutions.
- 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, logging of model usage, and predictions provided.
- Improve ML pipeline documentation and understandability.
- 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.
MINIMUM QUALIFICATIONS:
- K nowledge of project /program management terminology /concepts and the ability to leverage this knowledge to ensure the success of initiatives.
- 4 \+ years professional experience as a software engineer , data scientist , or AI Engineer .
- Strong background in statistical modeling including ARIMA, regression, and Random Forest models.
- Background in optimization problems is strongly preferred.
- Track record of producing machine learning models and agentic solutions 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 .
- Bachelors Degree
- Comfort working with a Windows computer and Microsoft Office Suite
- Comfort working with international partners and potentially flexing your calendar to meet the time zone needs.
PREFERRED QUALIFICATIONS:
- Bachelors Degree in Computer Science, Engineering, IT or other related fields
- 7\+ years of related experience as a software engineer, data scientist or AI Engineer
- Experience with deep code or custom code agentic frameworks (LangChain or LangGraph)
- Experience productionizing Optimization Models
- Experience with factory systems
- Experience with real\-time pipelines
ADDITIONAL CONSIDERATIONS:
- We are open to remote employees within the United States with a preference for talent in the Minneapolis market.
- International relocation or international remote working arrangements (outside of the US) will not be considered.
- Applicants for this position must be currently authorized to work in the United States on a full\-time basis. General Mills will not sponsor applicants for this position for work visas.
ELIGIBILITY
Applicants must meet a minimum 18\-year age qualification.
EQUAL OPPORTUNITY EMPLOYER (EOE)
General Mills is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, creed, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, marital or familial status, status regarding public assistance, or membership or activity in a local human rights commission.
REASONABLE ACCOMODATION REQUEST
If you need to request an accommodation during the application or hiring process, please fill out our online accommodation request form by following this link: Accommodation Request .
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At General Mills, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
General Mills AI Hiring
General Mills has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Minneapolis, MN, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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