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Python Ml Developer Jobs (NOW HIRING)

Strong proficiency in programming languages such as Python, R, Java, and C/C++. * Front-end ... ML Frameworks: Proficiency in ML Modeling concepts and frameworks like Sklearn, TensorFlow, Keras ...

Python, TypeScript, LangChain to name a few. Must Have Required skills * AI / ML experience BEFORE 2023 (non-negotiable) * Strong hands-on coding engineer (not just architecture or MLOps) * GitHub ...

Required : • 5+ years of hands on with ML technologies. • Expert Python and strong experience ... • Strong SQL/data engineering experience; proficiency with Spark/Databricks. • Cloud ...

Strong proficiency in programming languages such as Python, R, Java, and C/C++. * Front-end ... ML Frameworks: Proficiency in ML Modeling concepts and frameworks like Sklearn, TensorFlow, Keras ...

AI ML Developer 1 Key Responsibilities: - Design, develop, and deploy machine learning models for ... in Python and core machine learning libraries (e.g., scikit-learn, PyTorch, TensorFlow ...

Embedded AI/ML Developer

Spring, TX · On-site

$117K - $154K/yr

Embedded AI/ML Developer Description - The Embedded AI/ML Developer will design, develop, and ... Proficient in C/C++ and Python; familiar with embedded scripting, automation, build systems, and ...

ML/AI Developer (This role is open to US Citizens, Green Card holders, GC-EAD only. We do not ... Hands-on experience with Deep Learning, LLM, Python, TensorFlow, PyTorch and other AI frameworks

Python/AI/ML Engineer They are looking for an expert engineer ... developer with a year or two experience with generative AI (genAI is only a few years old from the ...

Strong proficiency in programming languages such as Python, R, Java, and C/C++. * Front-end ... ML Frameworks: Proficiency in ML Modeling concepts and frameworks like Sklearn, TensorFlow, Keras ...

ML/AI Developer (This role is open to US Citizens, Green Card holders, GC-EAD only. We do not ... Hands-on experience with Deep Learning, LLM, Python, TensorFlow, PyTorch and other AI frameworks ...

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Python Ml Developer information

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How much do python ml developer jobs pay per hour?

As of Sep 10, 2026, the average hourly pay for python ml developer in the United States is $58.62, according to ZipRecruiter salary data. Most workers in this role earn between $48.32 and $66.59 per hour, depending on experience, location, and employer.

What does a Python ML Developer do?

A Python ML Developer designs, builds, and deploys machine learning models using the Python programming language. They work with large datasets, clean and process data, select appropriate algorithms, and use libraries like TensorFlow, PyTorch, or scikit-learn to implement solutions. Their work often involves collaborating with data scientists and engineers to integrate machine learning models into applications. Additionally, they may be responsible for testing, tuning, and optimizing models to achieve the best possible performance in real-world scenarios.

What are the key skills and qualifications needed to thrive as a Python ML Developer?

To thrive as a Python ML Developer, you need strong programming skills in Python, a solid understanding of machine learning algorithms, and a background in mathematics or statistics, often supported by a degree in computer science, engineering, or a related field. Familiarity with tools and libraries such as TensorFlow, scikit-learn, PyTorch, and version control systems like Git is essential, along with experience using data visualization and cloud platforms. Critical soft skills include problem-solving, adaptability, and effective communication to collaborate with cross-functional teams and explain complex models to stakeholders. These skills ensure the successful development, deployment, and maintenance of machine learning solutions that drive business value.

What are some common challenges Python ML Developers face when deploying machine learning models to production?

Python ML Developers often encounter challenges such as ensuring model scalability, managing dependencies, and maintaining reproducibility when deploying models into production environments. Integrating machine learning models with existing systems can require close collaboration with DevOps and software engineering teams to streamline workflows and automate deployment pipelines. Additionally, monitoring model performance over time and handling data drift are crucial responsibilities to ensure continued accuracy and reliability of deployed solutions.

What is the difference between Python Ml Developer vs Data Scientist?

AspectPython Ml DeveloperData Scientist
Required CredentialsBachelor's in CS, Data Science, or related; Python, ML certificationsBachelor's/Master's in Data Science, Statistics, or related; Python, ML certifications
Work EnvironmentSoftware development teams, AI/ML projectsResearch, data analysis, modeling teams
Employer & Industry UsageTech companies, startups, AI firmsFinance, healthcare, tech, research institutions
Common Search & ComparisonYesYes

Python ML Developers focus on building and deploying machine learning models using Python, often working closely with software engineering teams. Data Scientists analyze data, create models, and generate insights, often using Python along with statistical tools. While both roles require Python and ML knowledge, Python ML Developers are more involved in implementation and deployment, whereas Data Scientists focus on data analysis and research.

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Infographic showing various Python Ml Developer job openings in the United States as of September 2026, with employment types broken down into 3% Internship, 88% Full Time, 4% Part Time, and 5% Contract. Highlights an 76% Physical, 5% Hybrid, and 19% Remote job distribution, with an average salary of $121,932 per year, or $58.6 per hour.

Data Scientist - Python, ML

Des Moines, IA • On-site

1 point system
IT Services • 51 - 200 employees

Contractor

Re-posted 27 days ago


Job description

Technical

  • ML for business applications: model mechanics, problem framing, evaluation, and iterative improvement
  • Statistics & modeling fundamentals (avoid black-box empiricism)
  • Feature engineering & selection: importance analysis, quantifying impact of feature changes
  • ML lifecycle on Databricks: experiment tracking, metrics, train/test/validation discipline, temporal validation

  • Python coding (pandas, scikit-learn, MLflow)



 

Behavioral

  • Works well in a team with a tech leader setting priorities and providing feedback
  • Curiosity to learn/understand more about the business problem, balanced with pragmatism to deliver what your responsible for
  • Ownership of outcomes
  • Works well in a structured project management framework: agile, standups, planning, task tracking

 

Nice to Have

  • Ag / seed domain familiarity