Senior Level Machine Learning Engineering Manager Jobs
Senior level machine learning engineering manager jobs demand end-to-end ownership of ML systems, from research direction through production deployment, with accountability for team performance and organizational outcomes. Openings cover 38% remote and hybrid settings across Technology & Software, Consulting & Professional Services, and Retail, with employers like Riot Games, EY, and Wipro hiring at this level now.
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Showing 5 of 11+ Senior Level Machine Learning Engineering Manager jobs
INTRODUCTION
McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve – we care.
What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow’s health today, we want to hear from you.
ABOUT THE ROLE
McKesson is seeking a Sr. Specialist, Machine Learning Engineering to design, build, and scale production-grade AI, machine learning, and generative AI solutions that deliver meaningful business impact. In this role, you will lead the development of scalable ML platforms, production pipelines, model deployment frameworks, and MLOps capabilities while partnering with cross-functional teams to solve complex business and technology challenges.
You will provide technical leadership across the full AI lifecycle, from experimentation and model development through deployment, observability, governance, and continuous improvement.
- Lead the design, development, deployment, and operation of enterprise-scale AI/ML and LLM solutions.
- Build and productionize machine learning pipelines using Azure Machine Learning, Azure Databricks, and cloud-native services.
- Establish and enhance MLOps and LLMOps practices including CI/CD, model versioning, automated testing, monitoring, and governance.
- Develop scalable deployment architectures using containerization, infrastructure as code, and automation frameworks.
- Partner with data scientists and product teams to transition prototypes into reliable production systems.
- Implement model observability, performance monitoring, drift detection, and reliability engineering practices.
- Lead technical troubleshooting, root cause analysis, and continuous improvement efforts for AI-enabled platforms.
- Mentor engineers and promote engineering excellence through documentation, knowledge sharing, and best practices.
BASIC REQUIREMENTS
- 7+ years of experience designing, building, and supporting machine learning solutions in enterprise environments.
- Bachelor's degree in Computer Science, Engineering, Data Science, Information Systems, or a related field, or equivalent experience.
- Strong software engineering experience with Python and modern application development practices.
- Experience deploying machine learning models into production cloud environments.
- Hands-on experience with MLOps including CI/CD pipelines, model lifecycle management, automation, and monitoring.
- Experience with Azure Machine Learning, Azure Databricks, or comparable cloud-based machine learning platforms.
- Experience with containerization technologies such as Docker and production deployment patterns.
- Ability to communicate technical concepts effectively to both technical and business stakeholders.
PREFERRED SKILLS/EXPERIENCE
- Experience developing and deploying Generative AI and Large Language Model (LLM) solutions.
- Experience with Azure AI Foundry, prompt engineering, evaluation frameworks, and AI guardrails.
- Knowledge of LLMOps practices, RAG architectures, model evaluation, and AI observability.
- Experience with Terraform or other Infrastructure-as-Code tools.
- Experience with Kubernetes, AKS, distributed computing, or large-scale data processing platforms.
- Familiarity with AI governance, responsible AI, security, privacy, and compliance practices.
- Experience in healthcare, supply chain, pharmaceutical, or distribution industries.
- Prior experience mentoring engineers and leading complex, cross-functional initiatives.
TRAVEL / WORK ENVIRONMENT / PHYSICAL REQUIREMENTS
- Travel may be required based on business needs.
- Position may be remote, hybrid, or office-based depending on organizational requirements.
- Must be able to perform standard computer-based work activities and communicate effectively with internal and external stakeholders.
At McKesson, we care about the well-being of the patients and communities we serve, and that starts with caring for our people. That’s why we have a Total Rewards package that includes comprehensive benefits to support physical, mental, and financial well-being. Our Total Rewards offerings serve the different needs of our diverse employee population and ensure they are the healthiest versions of themselves.
As part of Total Rewards, we are proud to offer a competitive compensation package at McKesson. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered.
OUR BASE PAY RANGE FOR THIS POSITION
€72,000 - €120,000
McKesson has become aware of online recruiting-related scams in which individuals who are not affiliated with or authorized by McKesson are using McKesson’s (or affiliated entities, like CoverMyMeds or RxCrossroads) name in fraudulent emails, job postings or social media messages. In light of these scams, please bear the following in mind:
McKesson Talent Advisors will never solicit money or credit card information in connection with a McKesson job application.
McKesson Talent Advisors do not communicate with candidates via online chatrooms or using email accounts such as Gmail or Hotmail. Note that McKesson does rely on a virtual assistant (Gia) for certain recruiting-related communications with candidates.
McKesson job postings are posted on our career site: careers.mckesson.com.
BENEFITS
- Coverage you can rely on
- Medical, Dental, and Vision
- Health Spending Accounts
-
Flexible Spending Accounts
-
Benefits that go beyond your base pay
- 401(k) (U.S.)
- Pension (Canada)
-
Employee Stock Purchase Plan
-
Support for total well-being
- Mental Health Programs
- Flexible Schedules
- Paid Time Off
- Wellness Program
- Education Reimbursement
- Volunteer Opportunities
-
Flexible Work Environment
-
A culture powered by belonging
By fostering a culture of belonging, we advance our goal of being the best place to work in healthcare—connecting the right talent to the right roles to solve our most critical challenges.
INTRODUCTION
McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve – we care.
What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow’s health today, we want to hear from you.
ABOUT THE ROLE
McKesson is seeking a Sr. Specialist, Machine Learning Engineering to design, build, and scale production-grade AI, machine learning, and generative AI solutions that deliver meaningful business impact. In this role, you will lead the development of scalable ML platforms, production pipelines, model deployment frameworks, and MLOps capabilities while partnering with cross-functional teams to solve complex business and technology challenges.
You will provide technical leadership across the full AI lifecycle, from experimentation and model development through deployment, observability, governance, and continuous improvement.
- Lead the design, development, deployment, and operation of enterprise-scale AI/ML and LLM solutions.
- Build and productionize machine learning pipelines using Azure Machine Learning, Azure Databricks, and cloud-native services.
- Establish and enhance MLOps and LLMOps practices including CI/CD, model versioning, automated testing, monitoring, and governance.
- Develop scalable deployment architectures using containerization, infrastructure as code, and automation frameworks.
- Partner with data scientists and product teams to transition prototypes into reliable production systems.
- Implement model observability, performance monitoring, drift detection, and reliability engineering practices.
- Lead technical troubleshooting, root cause analysis, and continuous improvement efforts for AI-enabled platforms.
- Mentor engineers and promote engineering excellence through documentation, knowledge sharing, and best practices.
BASIC REQUIREMENTS
- 7+ years of experience designing, building, and supporting machine learning solutions in enterprise environments.
- Bachelor's degree in Computer Science, Engineering, Data Science, Information Systems, or a related field, or equivalent experience.
- Strong software engineering experience with Python and modern application development practices.
- Experience deploying machine learning models into production cloud environments.
- Hands-on experience with MLOps including CI/CD pipelines, model lifecycle management, automation, and monitoring.
- Experience with Azure Machine Learning, Azure Databricks, or comparable cloud-based machine learning platforms.
- Experience with containerization technologies such as Docker and production deployment patterns.
- Ability to communicate technical concepts effectively to both technical and business stakeholders.
PREFERRED SKILLS/EXPERIENCE
- Experience developing and deploying Generative AI and Large Language Model (LLM) solutions.
- Experience with Azure AI Foundry, prompt engineering, evaluation frameworks, and AI guardrails.
- Knowledge of LLMOps practices, RAG architectures, model evaluation, and AI observability.
- Experience with Terraform or other Infrastructure-as-Code tools.
- Experience with Kubernetes, AKS, distributed computing, or large-scale data processing platforms.
- Familiarity with AI governance, responsible AI, security, privacy, and compliance practices.
- Experience in healthcare, supply chain, pharmaceutical, or distribution industries.
- Prior experience mentoring engineers and leading complex, cross-functional initiatives.
TRAVEL / WORK ENVIRONMENT / PHYSICAL REQUIREMENTS
- Travel may be required based on business needs.
- Position may be remote, hybrid, or office-based depending on organizational requirements.
- Must be able to perform standard computer-based work activities and communicate effectively with internal and external stakeholders.
At McKesson, we care about the well-being of the patients and communities we serve, and that starts with caring for our people. That’s why we have a Total Rewards package that includes comprehensive benefits to support physical, mental, and financial well-being. Our Total Rewards offerings serve the different needs of our diverse employee population and ensure they are the healthiest versions of themselves.
As part of Total Rewards, we are proud to offer a competitive compensation package at McKesson. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered.
OUR BASE PAY RANGE FOR THIS POSITION
€72,000 - €120,000
McKesson has become aware of online recruiting-related scams in which individuals who are not affiliated with or authorized by McKesson are using McKesson’s (or affiliated entities, like CoverMyMeds or RxCrossroads) name in fraudulent emails, job postings or social media messages. In light of these scams, please bear the following in mind:
McKesson Talent Advisors will never solicit money or credit card information in connection with a McKesson job application.
McKesson Talent Advisors do not communicate with candidates via online chatrooms or using email accounts such as Gmail or Hotmail. Note that McKesson does rely on a virtual assistant (Gia) for certain recruiting-related communications with candidates.
McKesson job postings are posted on our career site: careers.mckesson.com.
BENEFITS
- Coverage you can rely on
- Medical, Dental, and Vision
- Health Spending Accounts
-
Flexible Spending Accounts
-
Benefits that go beyond your base pay
- 401(k) (U.S.)
- Pension (Canada)
-
Employee Stock Purchase Plan
-
Support for total well-being
- Mental Health Programs
- Flexible Schedules
- Paid Time Off
- Wellness Program
- Education Reimbursement
- Volunteer Opportunities
-
Flexible Work Environment
-
A culture powered by belonging
By fostering a culture of belonging, we advance our goal of being the best place to work in healthcare—connecting the right talent to the right roles to solve our most critical challenges.
See All 11 Senior Level Machine Learning Engineering Manager Jobs
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Top Industries Hiring
- Technology & Software
- Consulting & Professional Services
- Retail
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Senior Level Machine Learning Engineering Manager Jobs: Frequently Asked Questions
How do I get a senior level machine learning engineering manager job?
Employers at this level look for candidates who have shipped production ML systems at scale and led engineering teams through the full model lifecycle. Demonstrating cross-functional leadership, a history of translating ambiguous business problems into technical roadmaps, and experience mentoring engineers gives candidates the clearest edge. Strong written design documentation and evidence of measurable model impact round out a competitive profile.
Which companies hire senior level machine learning engineering managers?
Companies hiring senior level machine learning engineering managers right now include Riot Games, EY, and Wipro, based on current listings on Migrate Mate as of November 2026. Hiring at this level comes from a mix of large technology firms, well-funded AI-native startups, and enterprise companies with established data and ML platform teams.
Are there remote senior level machine learning engineering manager jobs?
Yes, remote and hybrid availability is strong at this level. About 38% of senior level machine learning engineering manager openings are remote or hybrid as of November 2026, reflecting how many organizations have built distributed ML teams. On-site roles tend to cluster around research labs, regulated industries, and teams with significant hardware or infrastructure dependencies.
What makes a machine learning engineering manager role senior level?
Senior level machine learning engineering manager roles are defined by scope and ownership rather than task execution. Expectations include setting the technical direction for ML platforms or product areas, managing and growing a team of engineers and researchers, driving alignment with product and business stakeholders, and making architectural decisions with long-term consequences. Mid-level roles operate within defined scope, senior roles define and expand it.
Which industries hire the most senior level machine learning engineering managers?
Senior Level machine learning engineering manager roles concentrate in Technology & Software, Consulting & Professional Services, and Retail, based on current listings on Migrate Mate as of November 2026. These sectors drive hiring at this level because they have the data infrastructure, product complexity, and organizational scale that require dedicated ML leadership rather than individual contributors alone.