MCKESSON

location-iconMCKESSON

Machine Learning Engineer

location-iconFredericksburg, VA, 22408

jobtype-iconPart Time, Full Time

estimated-salary-icon$63,602 per year

dateposted-iconPosted 8 days ago

Apply Now

location-iconActively Hiring

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.

Position Summary:

The Machine Learning Engineer plays a pivotal role in advancing AI solutions at McKesson. This position involves applying AI methodologies to address interdisciplinary business issues in Finance, Operations, Accounting, and Supply Chain sectors.

As part of this position, you will be responsible for conducting research, testing concepts, designing technical solutions, engaging in hands-on development, and optimizing AI-driven automation systems. Strong collaboration abilities are required to work closely with cross-functional teams, including UX Design professionals, the AI Center of Excellence (CoE), and Quality Analysts, to deliver intelligent automation solutions that elevate our business processes and outcomes. Strong cloud development skills and experience with AI and automation technologies are crucial, along with the eagerness and ability to quickly learn. With a focus on innovation and a commitment to excellence, you will need to understand business challenges in order to provide value through highly scalable and available automation solutions.

Responsibilities include:

  • Craft computer vision models specifically for image and video analysis and deploy them accordingly.
  • Construct and apply techniques for detecting, recognizing, and tracing objects with precision.
  • Fine-tune image processing approaches to boost overall precision and efficiency levels.
  • Ensure that the solutions have the capacity to efficiently process and handle extensive datasets in a scalable manner.
  • Keep abreast of the most recent developments in Foundation Model studies and incorporate them into your work assignments.
  • Engage in collaboration with AI professionals to create and deliver innovative artificial intelligence products.
  • Utilize the agile scrum approach.
  • Partner with the engineering team in delivering models for use in production applications
  • Familiarity with data engineering principles encompassing data ingestion, ETL processing, and data modeling to maintain efficient data pipelines in Databricks.
  • Efficiently handle convoluted project requirements, deadlines, and contingencies to enable organized planning and attainment.
  • Takes the initiative to learn and apply new technology, concepts, and procedures as needed for the project. Maintains a current understanding of the technology trends relevant to the industry.
  • Captures, archives, and distributes successful practices to other McKesson automation teams.

We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. 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. For more information regarding benefits at McKesson, please

Our Base Pay Range for this position

$103,300 - $172,100

McKesson is an Equal Opportunity Employer

McKesson is devoted to granting all applicants and employees equal employment prospects, striving to nurture a diverse and inclusive arena free from discrimination based on race, color, religion, gender, sexual orientation, national origin, veteran status, disability, age, or genetic information. For more details on McKesson's complete Equal Employment Opportunity guidelines, please visit our dedicated page.

Embrace the chance to join McKesson!

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FAQ's

Find the answers for the most frequently asked questions below

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While the job title "Learning Engineer" is not directly related to engineering as a field, it does involve the design, development, and implementation of learning solutions for engineering professionals. However, to become an engineer, one typically needs a strong foundation in mathematics and science, as well as specific engineering knowledge in a chosen field such as mechanical, electrical, or civil engineering. So, while anyone can learn engineering, it requires a specific educational background and skillset.

While a master's degree can be beneficial for a Machine Learning Engineer role, it is not strictly necessary. Many Machine Learning Engineers have a background in computer science, mathematics, statistics, or a related field. Practical experience with machine learning algorithms, programming skills, and a strong understanding of data structures and algorithms are often more important than a master's degree. However, a master's degree can provide additional knowledge and specialization in the field.

While a degree can be beneficial, it's not strictly necessary to become a Learning Engineer. Relevant experience, strong programming skills, and a solid understanding of machine learning principles are more important. Certifications and online courses can also demonstrate competency in the field. However, a degree can still provide additional advantages in the job market.

As a learning engineer, one learns about instructional design, educational technology, learning analytics, and adult learning principles to create, implement, and evaluate effective learning experiences in various educational or corporate training contexts.

Engineering students typically learn subjects such as mathematics, physics, and computer science, as well as specialized engineering topics like mechanics, materials science, and engineering design, depending on their chosen field of study (e.g., electrical, mechanical, civil, computer, etc.). The goal is to equip them with the knowledge and skills necessary to design, build, and test engineering systems.

A learning engineer designs, develops, and implements machine learning models and systems to improve and optimize various processes, often in the context of education or training, but can also be applied to other fields such as data analysis or artificial intelligence.

Learning engineers typically study a combination of subjects in school, including: 1. Mathematics (Calculus, Linear Algebra, Statistics) 2. Computer Science (Programming, Data Structures, Algorithms) 3. Electrical Engineering (Circuits, Systems, Signals & Systems) 4. Machine Learning (Neural Networks, Reinforcement Learning, Deep Learning) 5. Software Engineering (Software Development, Design Patterns, Agile Methodologies) 6. Artificial Intelligence (AI Principles, Robotics, Natural Language Processing) 7. Data Science (Data Mining, Data Visualization, Machine Learning Algorithms) 8. Human-Computer Interaction (UX Design, Usability, Accessibility) These subjects provide a strong foundation for a learning engineer to develop, implement, and evaluate machine learning models and systems.

A Learning Engineer designs, develops, and maintains machine learning systems and applications. They often work on projects that involve data analysis, model building, and deployment, with a focus on making machine learning solutions scalable, efficient, and user-friendly.

A learning engineer is a professional who designs, develops, and implements educational technology solutions to enhance learning experiences and improve educational outcomes. They often work in educational institutions or learning technology companies, using their skills in instructional design, technology, and data analysis to create effective learning environments.

While a software engineer and a mechanical engineer are distinct professions, it is possible for a software engineer to transition to a mechanical engineering role. This would require additional education in mechanical engineering principles, as well as practical experience in mechanical design and engineering. However, the specifics of such a transition would depend on the individual's aptitude, interest, and the specific requirements of the mechanical engineering field.