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About This Role
We anticipate the application window for this opening will close on \- 27 Jul 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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MiniMed is seeking a Senior AI/Data Science Engineer – Manufacturing \& Operations to join our Data Science team in a dedicated capacity supporting manufacturing, supply chain, operations, and new product introduction (NPI) functions. This role is responsible for accelerating the evolution of our cloud data infrastructure for manufacturing data sources, solidifying trusted operational metrics, supporting root cause investigations with advanced analytics, and progressively delivering predictive and AI\-driven solutions that improve how we design, build, and deliver our products. The ideal candidate is a technically strong data scientist with experience in supply chain, manufacturing, or a related industry who thrives in complex, cross\-functional environments.
Responsibilities may include the following and other duties may be assigned.
- Contribute to the maturation of MiniMed's manufacturing data pipelines, identifying critical data elements required for advanced analytics and partnering with data engineering teams to drive changes through requirements, development, validation, and production deployment
- Deliver measurable improvements in operational excellence and cost efficiency by converting complex operational datasets into actionable, governed insights \- rationalizing fragmented reporting, establishing authoritative source\-of\-truth metrics, and enabling confident decision\-making across the organization
- Provide advanced analytical support for root cause investigations, bringing statistical rigor and computational depth to isolate sources of variation and accelerate resolution of critical quality and process challenges
- Identify opportunities and implement modern AI and machine learning integration across manufacturing, supply chain, operations, and NPI functions, balancing technical ambition against practical realities including data readiness, regulatory considerations, and demonstrable business value. Target new technology implementation towards problem solving.
- Serve as the dedicated data science partner to operational functions, developing deep domain fluency and trusted cross\-functional relationships; advance the maturity of the broader Data Science team through reusable tooling, documentation standards, and technical mentorship
Required Knowledge and Experience:
Bachelor’s degree with 4\+ years of relevant experience; or advanced degree with 2\+ years of relevant experience.
Preferred Qualifications:
- Bachelor's degree in Data Science, Statistics, Computer Science, Industrial Engineering, or a related field, Master's or Ph.D. in quantitative discipline preferred
- 5 or more years of applied data science or machine learning engineering experience, with meaningful experience in a manufacturing, supply chain, or industrial operations environment
- Familiarity with manufacturing execution system (MES) and ERP systems data structures
- Proficient in Python for data science and ML development, including libraries such as pandas, scikit\-learn, PyTorch, and TensorFlow
- Strong SQL skills and demonstrated experience working with cloud data platforms such as Databricks, Snowflake, Azure, and AWS
- Experience developing and deploying production machine learning models and analytical pipelines
- Exposure to computer vision, NLP, or generative AI applications in an industrial or operational context
- Experience with BI and data visualization tools such as Power BI or Tableau
- Demonstrated ability to communicate complex analytical concepts clearly to non\-technical business and operations stakeholders
- Proven ability to work effectively in cross\-functional, matrixed environments with multiple competing priorities and ambiguous problem definitions
- Solid foundation in statistical methods including hypothesis testing, regression analysis, statistical process control, design of experiments, and process capability analysis
- Experience working in a regulated industry such as medical devices, pharmaceuticals, or aerospace, with familiarity with FDA data integrity requirements or GxP standards
- Familiarity with MLOps platforms such as MLflow, Azure Machine Learning, or AWS SageMaker
- Experience with AI\-assisted coding environments such as Windsurf, GitHub Copilot, or similar
- Experience working with IoT or IoT sensor data and time\-series data pipelines
- Knowledge of Lean Manufacturing or Six Sigma methodologies; Green Belt or Black Belt certification a plus
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):$112,000\.00 \- $190,000\.00
For roles located in California, Seattle WA, Washington DC, Boston MA, and New York City, the salary range is $124,000\.00 \- $212,000\.00 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 $112K-$212K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($162K) sits 26% below the category median. Disclosed range: $112K to $212K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
MiniMed AI Hiring
MiniMed has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Northridge, CA, US. Compensation range: $212K - $212K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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