Applied Machine Learning information
See Indiana salary details
$24.3K - $29.7K
5% of jobs
$31.5K is the 25th percentile. Wages below this are outliers.
$29.7K - $35.1K
59% of jobs
$35.1K - $40.5K
9% of jobs
$40.9K is the 75th percentile. Wages above this are outliers.
$40.5K - $45.9K
17% of jobs
$45.9K - $51.3K
4% of jobs
$51.3K - $56.7K
2% of jobs
$56.7K - $62.1K
3% of jobs
$62.1K - $67.5K
0% of jobs
$67.5K - $72.9K
0% of jobs
$72.9K - $78.3K
0% of jobs
$78.3K - $83.7K
0% of jobs
How much do applied machine learning jobs pay per year?
As of Aug 18, 2026, the average yearly pay for applied machine learning in Indiana is $40,521.00, according to ZipRecruiter salary data. Most workers in this role earn between $30,900.00 and $43,800.00 per year, depending on experience, location, and employer.
Applied machine learning involves using machine learning techniques and algorithms to solve real-world problems in various industries, such as healthcare, finance, and technology. Practitioners focus on selecting appropriate models, preparing data, training algorithms, and deploying solutions that deliver tangible value. Unlike theoretical machine learning, applied machine learning emphasizes practical implementation, evaluation, and optimization to meet business or research objectives.
To excel in Applied Machine Learning, you need a solid background in mathematics, statistics, computer science, and experience with machine learning algorithms, often supported by a relevant degree or certification. Familiarity with programming languages like Python or R, frameworks such as TensorFlow or PyTorch, and version control systems is typically required. Strong problem-solving abilities, communication skills, and a collaborative mindset help you interpret results and convey insights to diverse stakeholders. These competencies are crucial for building effective models, driving data-driven decisions, and ensuring the successful integration of machine learning solutions into real-world applications.
Applied Machine Learning engineers often work closely with cross-functional teams including data scientists, software engineers, product managers, and business analysts. They are typically responsible for translating business problems into machine learning solutions and ensuring models are effectively integrated into production systems. This role requires frequent communication to align on project goals, share progress, and address technical challenges, making teamwork and stakeholder management crucial for successful deployments and continuous improvement.
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