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About This Role
We anticipate the application window for this opening will close on \- 22 Jun 2026
At MiniMed, you can begin a lifelong career of exploration and innovation, while helping make a difference in the lives of people living with diabetes around the globe. You'll lead with purpose, breaking down barriers to innovation for a more connected, compassionate world.
About the Role
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The Senior Program Manager, AI Products is responsible for leading the planning and cross\-functional execution of enterprise AI initiatives across MiniMed business functions, including Commercial, Marketing, Finance, and Quality. This role serves as a strategic and technical bridge between business stakeholders, AI Engineering, Data Science, Product, and supporting enterprise teams within MiniMed's Transformation Organization.
This leader drives the end\-to\-end delivery of AI product and program outcomes from use case definition and business case development through implementation, adoption, value realization, and continuous improvement. The role is accountable for aligning priorities across teams, establishing strong program governance, proactively managing risks and dependencies, and ensuring AI initiatives deliver measurable business value in a regulated MedTech environment.
The Senior Program Manager, AI Products shapes the enterprise AI portfolio by connecting business priorities to technical capabilities, helping functions define success metrics, and ensuring initiatives are coordinated with broader roadmap, readiness, compliance, and change adoption needs.
This role requires a strong blend of product thinking, strategic problem solving, operational rigor, and executive presence. The ideal candidate is comfortable working through ambiguity, influencing across functions, and turning broad ideas into practical, scalable programs.
This position is hybrid onsite in Atlanta, GA or Northridge, CA
Responsibilities may include the following and other duties may be assigned.
AI Strategy, Portfolio Planning \& Prioritization (25%)
- Lead the planning and evolution of the AI products and initiatives portfolio across Commercial, Marketing, Finance, Quality, and other business functions
- Partner with business leaders and technical teams to identify, evaluate, prioritize, and sequence high\-value AI opportunities with clear business outcomes and measurable success criteria
- Translate complex business needs into scalable AI product and program opportunities, balancing strategic value, feasibility, risk, and organizational readiness
- Build business cases for investment, prioritize work against resource constraints, and help manage budget tradeoffs
- Define and track KPIs to measure adoption, value realization, and operational impact
- Translate ambiguous business problems into clear product plans, success metrics, and execution roadmaps, including release management, testing, training, compliance, and adoption activities
- Communicate clearly and effectively with senior leadership, including concise recommendations and actionable next steps
End\-to\-End Program Execution \& Delivery (25%)
- Lead cross\-functional execution of AI initiatives from discovery and data readiness through deployment, adoption, monitoring, and continuous improvement
- Build and manage integrated program plans that coordinate deliverables, milestones, dependencies, decisions, and risks across business and technical teams
- Partner closely with AI Engineering, Data Science, Product, Architecture, Security, and business stakeholders to ensure delivery alignment and execution discipline
- Drive issue resolution, escalation management, and proactive risk mitigation to maintain delivery momentum and predictability
- Ensure AI initiatives are implemented responsibly, with appropriate attention to quality, safety, privacy, compliance, and change management
Governance, Operating Rhythm \& Cross\-Functional Alignment (20%)
- Establish and lead program governance structures, decision\-making forums, and operating rhythms that enable transparency, alignment, and timely execution
- Drive executive reporting, including program updates, risks, tradeoffs, milestones, and decision points
- Coordinate planning and progress reviews across multiple teams to maintain alignment on scope, milestones, dependencies, and outcomes
- Create clear mechanisms for tracking actions, risks, dependencies, and executive decisions across the portfolio
- Drive alignment between strategic objectives and execution plans, ensuring teams remain focused on the highest\-value outcomes
Business Partnership, Readiness \& Change Adoption (15%)
- Build trusted partnerships with business leaders and functional stakeholders to shape AI use cases, operating models, and implementation approaches
- Lead discovery and planning workshops to refine business problems, define target outcomes, and clarify success measures
- Develop business cases that include cost\-benefit analysis, expected efficiency gains, operational impact, and risk\-adjusted value
- Partner with stakeholders to drive organizational readiness, process alignment, training inputs, and change adoption plans for AI\-enabled capabilities
- Ensure business and technical teams remain aligned throughout execution, launch, and post\-launch stabilization
Metrics, Insights \& Value Realization (15%)
- Define and track KPIs for AI initiatives, including adoption, business outcomes, automation impact, model performance, operational effectiveness, and portfolio value realization
- Develop reporting and review mechanisms that provide leadership with clear visibility into program health, outcomes, and ROI
- Use performance insights, experimentation results, and stakeholder feedback to inform roadmap decisions and continuous improvement
- Monitor whether deployed capabilities are delivering intended value and coordinate corrective actions when needed
MINIMUM QUALIFICATIONS
- Bachelor’s degree in Computer Science, Data Science, Engineering, Information Systems, Business, or a related field and 7\+ years of experience in program management, technical program management, product management, or related strategic delivery roles with progressively increasing scope and complexity
- Or, advanced degree in Computer Science, Data Science, Engineering, Information Systems, Business, or a related field and 7\+ years of experience in program management, technical program management, product management, or related strategic delivery roles with progressively increasing scope and complexity
PREFERRED QUALIFICATIONS
- Proven track record of leading enterprise AI or AI\-enabled product initiatives from concept through deployment and business adoption
- Demonstrated success coordinating work across multiple business functions such as Commercial, Finance, Marketing, Quality, or similar areas
- Excellent written and verbal communication skills, including experience preparing materials for executive audiences
- Experience building business cases, prioritizing investments, and managing tradeoffs across scope, budget, and timelines
- Strong understanding of the AI/ML lifecycle, including use case definition, data readiness, model development, deployment, monitoring, and ongoing optimization
- Familiarity with generative AI and LLM patterns such as retrieval\-augmented generation, fine\-tuning, prompt engineering, and embeddings
- Working knowledge of MLOps concepts, machine learning delivery pipelines, and associated governance considerations
- Exposure to modern data platforms and architecture concepts, including APIs, lakehouses, vector databases, and ETL/ELT pipelines
- Familiarity with cloud AI platforms such as Azure AI, AWS SageMaker, or Google Vertex AI
- Experience building integrated plans, managing dependencies, and driving execution across matrixed teams without direct reporting authority
- Strong communication, facilitation, stakeholder management, and executive presentation skills
- Proficiency with Agile and program delivery tools such as Jira, Confluence, and related planning platforms
- Master’s degree in Business Administration, Data Science, Artificial Intelligence, Engineering, or a related field
- Experience in a regulated industry such as medical devices, pharmaceuticals, or financial services
- Experience with enterprise AI governance, Responsible AI, model risk management, or related control frameworks
- Background in large\-scale digital transformation programs, including organizational readiness and change adoption
- Knowledge of enterprise generative AI platforms such as OpenAI, Azure OpenAI, or Anthropic Claude
- Exposure to CRM or ERP platforms such as Salesforce or SAP and their AI extension ecosystems
Preferred Certifications
- Project Management Professional (PMP), Program Management Professional (PgMP), or equivalent
- SAFe Agilist (SA) or similar enterprise delivery certification
- AWS, Azure, or GCP AI, Data, or Cloud certifications
Physical Job Requirements
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The above statements are intended to describe the general nature and level of work being performed by employees assigned to this position, but they are not an exhaustive list of all the required responsibilities and skills of this position.
The physical demands described within the Responsibilities section of this job description are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. For Office Roles: While performing the duties of this job, the employee is regularly required to be independently mobile. The employee is also required to interact with a computer and communicate with peers and co\-workers. Contact your manager or local HR to understand the Work Conditions and Physical requirements that may be specific to each role.
Benefits \& Compensation
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MiniMed offers a competitive salary and flexible benefits package
At MiniMed, we put people first. A commitment to our employees lives at the core of our values: We recognize their contributions. They share in the success they help create. We offer a wide range of benefits, resources, and competitive compensation plans designed to support you at every stage of your career and life.
Salary ranges for U.S (excl. PR) locations (USD):$136,000\.00 \- $230,000\.00
For roles located in California, Seattle WA, Washington DC, Boston MA, and New York City, the salary range is $150,000 \- $256,000 USD.
Actual compensation may vary based on factors including experience, education, certifications, skills, market conditions, internal equity, and geographic location. Compensation and benefits information pertains solely to candidates hired within the United States (local market compensation and benefits will apply for others).
This position is eligible for a short\-term incentive called the Short Term Incentive (STI).
At MiniMed, we are committed to supporting the well\-being and financial security of our employees. Regular employees working 20 or more hours per week are eligible for a robust benefits package, including health, dental, and vision insurance, as well as access to a Health Savings Account, Healthcare Flexible Spending Account, life insurance, long\-term disability leave, and a dependent daycare spending account. In addition, all regular employees enjoy incentive plans, a 401(k) plan with company match, short\-term disability coverage, paid time off and holidays, participation in our Employee Stock Purchase Plan, and access to our Employee Assistance Program. Eligible employees may also benefit from our Non\-qualified Retirement Plan Supplement and Capital Accumulation Plan, subject to IRS minimum earnings requirements. Please note that “regular employees” refers to those who are not temporary staff, such as interns, and some benefits may not apply to employees in Puerto Rico.
For further details about our comprehensive benefits, we encourage you to visit the link below.
MiniMed Benefits Overview
About MiniMed
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MiniMed is a full\-stack insulin delivery company dedicated to supporting people living with diabetes through every step of their journey — when and how they need it. For more than 40 years, we’ve been committed to redefining what’s possible: intelligent dosing systems designed for real life, predictive insights that stay a step ahead, and always on support when it’s needed most. At the heart of everything we do is a simple Mission: to make every day a better day for people with diabetes.
Learn more about our business, and our mission here .
It is the policy of MiniMed to provide equal employment opportunity (EEO) to all persons regardless of age, color, national origin, citizenship status, physical or mental disability, race, religion, creed, gender, sex, sexual orientation, gender identity and/or expression, genetic information, marital status, familial status, membership or activity in a local human rights commission, status with regard to public assistance, veteran status, or any other characteristic protected by federal, state, or local law. In addition, MiniMed will provide reasonable accommodations for qualified individuals with disabilities.
If you are applying to perform work for MiniMed in any position which will involve performing at least two (2\) hours of work on average each week within the unincorporated areas of Los Angeles County, you can find here a list of all material job duties of the specific job position which MiniMed reasonably believes that criminal history may have a direct, adverse and negative relationship potentially resulting in the withdrawal of a conditional offer of employment. MiniMed will consider for employment qualified job applicants with arrest or conviction records in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.
Salary Context
This $136K-$256K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At MiniMed, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($196K) sits 8% above the category median. Disclosed range: $136K to $256K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
MiniMed AI Hiring
MiniMed has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Los Angeles, CA, US. Compensation range: $256K - $256K.
Location Context
AI roles in Los Angeles pay a median of $191,580 across 1,792 tracked positions. That's 4% below the national median.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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