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Mlb Machine Learning Jobs (NOW HIRING)

Rapsodo

General Application (US)

Saint Louis, MO · On-site

... and machine learning to help all athletes maximize their performance. Our proprietary technology applications range from helping PGA Tour golfers optimize their launch conditions to allowing MLB ...

Rapsodo

General Application (US)

Saint Louis, MO

... and machine learning to help all athletes maximize their performance. Our proprietary technology applications range from helping PGA Tour golfers optimize their launch conditions to allowing MLB ...

Rapsodo

General Application (US)

Saint Louis, MO · On-site

... and machine learning to help all athletes maximize their performance. Our proprietary technology applications range from helping PGA Tour golfers optimize their launch conditions to allowing MLB ...

Apple

Component Quality Engineer

Cupertino, CA · On-site

$88K - $114K/yr

Team manages Apple suppliers semiconductor fab/assembly manufacturing and testing process, design debug and FA, quality monitoring, data analytics machine learning, MLB/IC EE, application level etc.

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All JobsMlb Machine Learning Jobs

Mlb Machine Learning information

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$25.5K

$42.6K

$88K

How much do mlb machine learning jobs pay per year?

As of Jun 8, 2026, the average yearly pay for mlb machine learning in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an MLB Machine Learning Engineer, and why are they important?

To excel as an MLB Machine Learning Engineer, you need strong skills in statistics, data analysis, programming (Python or R), and a background in machine learning or data science, often supported by a relevant degree. Familiarity with tools like TensorFlow, PyTorch, SQL databases, and cloud computing platforms is typically required, along with experience using baseball-specific data sources. Exceptional problem-solving abilities, teamwork, and clear communication set candidates apart in this data-driven field. These skills enable the development of advanced models that inform player evaluation, game strategy, and organizational decision-making within Major League Baseball.

What is an MLB Machine Learning job?

An MLB Machine Learning job involves applying data science and machine learning techniques to analyze baseball data for Major League Baseball teams or organizations. Professionals in this role use statistical models and algorithms to predict player performance, optimize strategies, and gain competitive advantages. Their work includes processing large datasets, creating predictive analytics, and collaborating with coaches, scouts, and analysts to inform decision-making. Typical tasks may also include developing tools for player scouting, injury prediction, and game strategy optimization.

What is the difference between Mlb Machine Learning vs Data Scientist?

AspectMlb Machine LearningData Scientist
Required CredentialsBachelor's or Master's in Computer Science, Data Science, or related fields; knowledge of ML algorithmsBachelor's or Master's in Data Science, Statistics, Computer Science; strong analytical skills
Work EnvironmentTech companies, sports analytics firms, research labsBusiness, finance, healthcare, tech industries
Employer & Industry UsageSports analytics, machine learning projects in sports industryData analysis across various industries including finance, healthcare, and tech

While both roles involve data analysis and machine learning, Mlb Machine Learning specializes in applying ML techniques specifically within the sports industry, particularly baseball analytics. Data Scientists have a broader scope, working across multiple industries with a focus on extracting insights from data. The credentials and work environments overlap significantly, but the industry focus distinguishes Mlb Machine Learning from general Data Scientist roles.

What are some common challenges faced by machine learning professionals working in Major League Baseball (MLB)?

Machine learning professionals in MLB often face challenges such as dealing with large and complex datasets from various sources, ensuring data quality, and translating analytical insights into actionable strategies for coaches and players. Collaboration across departments, including analytics, scouting, and coaching staff, is essential but can require strong communication skills to bridge technical and non-technical perspectives. Additionally, adapting models to the fast-paced and ever-evolving nature of the sport means continuous learning and iteration are critical for success.

General Application (US)

Rapsodo

Saint Louis, MO • On-site

Full-time

Posted 5 days ago

Job description

Rapsodo Inc. is a sports analytics company that uses computer vision and machine learning to help all athletes maximize their performance. Our proprietary technology applications range from helping PGA Tour golfers optimize their launch conditions to allowing MLB pitchers to increase the efficiency of their breaking balls. Current partners include all 30 MLB teams, MLB, USA Baseball, Golf Digest, PGA of America, and over 1000 NCAA athletic departments.
We are innovative, focused, and rapidly growing. We are continuously looking for very driven team players who will stop at nothing to deliver state-of-the-art solutions as part of Team Rapsodo. If you share our enthusiasm for sports and technology, we'd love to hear from you.
Why Rapsodo?
  • Be part of a company that is shaping the future of sports.
  • Access exciting growth opportunities in a fast-paced industry.
  • Contribute to cutting-edge innovation in sports technology.
  • Work alongside a passionate, sports-loving team.

If you're ready to bring your unique skills and experience to Rapsodo and help us take sports technology to the next level, send us your application!

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