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Python Machine Learning Jobs (NOW HIRING)

FastTek

Machine Learning Engineer

Dearborn, MI · On-site

Machine Learning Engineer #1058742 Position Description: We are seeking an experienced AI Engineer ... Strong Python programming experience (backend development, APIs, automation) * Experience deploying ...

Compunnel

Machine Learning Engineer

Philadelphia, PA · On-site

The ideal candidate will have strong expertise in Python, PySpark, AWS, and machine learning model development, with a proven track record of delivering production-ready models that drive business ...

Quantum Machines

Machine Learning Engineer

We are looking for a Machine Learning Engineer to design, build, and deploy machine learning ... Develop and maintain Python-based ML services and libraries that integrate with the wider Quantum ...

Pattern AI

Machine Learning Engineer

San Mateo, CA · On-site +1

Company Description PatternAI is an automated machine learning platform that reveals critical ... Fluency in Python coding as well as data manipulation (SQL, Spark, Pandas) * Broad familiarity with ...

Pattern AI

Machine Learning Engineer

San Mateo, CA · On-site

Company Description PatternAI is an automated machine learning platform that reveals critical ... Fluency in Python coding as well as data manipulation (SQL, Spark, Pandas) * Broad familiarity with ...

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How much do python machine learning jobs pay per hour?

As of Sep 12, 2026, the average hourly pay for python machine learning 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 is a Python machine learning?

A Python Machine Learning job involves developing, training, and deploying machine learning models using Python. Professionals in this role work with libraries like TensorFlow, scikit-learn, and PyTorch to analyze data, build predictive models, and optimize algorithms. Responsibilities often include data preprocessing, feature engineering, model evaluation, and deploying models to production environments. These roles are commonly found in industries like finance, healthcare, and e-commerce, where data-driven decision-making is crucial.

What does a typical workday look like for a Python machine learning professional?

A typical workday for a Python Machine Learning professional often involves tasks like cleaning and pre-processing data, developing and training machine learning models, and evaluating their performance using statistical metrics. You'll collaborate with data engineers, data scientists, and product managers to understand business requirements and integrate models into production environments. Regularly, you'll participate in code reviews, team meetings, and troubleshooting sessions to optimize model performance and address any issues. This dynamic role requires both independent project work and frequent cross-functional collaboration to ensure that solutions meet real-world needs.

What are the key skills and qualifications needed to thrive in the Python machine learning position, and why are they important?

To thrive as a Python Machine Learning professional, you need a strong background in statistics, programming (especially Python), data analysis, and machine learning algorithms, often supported by a degree in computer science, mathematics, or a related field. Proficiency in libraries and frameworks like scikit-learn, TensorFlow, PyTorch, and familiarity with data visualization and version control tools are highly valued, as are relevant certifications such as TensorFlow Developer or AWS Machine Learning. Strong problem-solving ability, effective communication, and teamwork skills are important for collaboration and translating technical findings to non-technical stakeholders. These competencies enable you to design, develop, and deploy robust machine learning models that drive business solutions and innovation.

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Python Machine Learning jobs near you
Infographic showing various Python Machine Learning job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 22% Part Time, and 2% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $121,932 per year, or $58.6 per hour.

Machine Learning

Manhattan, NY • On-site

Cantor Fitzgerald Securities
Finance and Insurance • 10K+ employees
Learn more about Cantor Fitzgerald Securitiesexternal

Full-time

Posted 11 days ago

Job description


Join Cantor Fitzgerald Technology Markets LLC as a Machine Learning Engineer focused on building AI-driven solutions for a high-volume financial services business. You will work closely with product, engineering, and business teams to create, test, and operationalize large language model (LLM) applications, ensuring they meet performance, reliability, and responsible-AI standards.
Responsibilities
  • Design and implement LLM-driven features in production systems.
  • Build and maintain data pipelines for both structured and unstructured data.
  • Write clean, testable Python code and maintain reusable libraries.
  • Develop prompts, tool-calling workflows, and retrieval pipelines.
  • Create evaluation suites, define success metrics, and analyze failures.
  • Diagnose and mitigate hallucination, latency, and cost issues.
  • Collaborate with product, engineering, and business stakeholders.
  • Implement monitoring, logging, and alerting for AI services.
  • Contribute to responsible-AI guardrails and human-in-the-loop processes.
  • Document designs, experiments, and findings for internal knowledge sharing.

Qualifications
  • Bachelor's degree in computer science, machine learning, mathematics, physics, statistics, econometrics, or equivalent practical experience.
  • Experience contributing to production or production-like software through work, internships, research, open source, or substantial personal projects.
  • Strong programming ability in Python with clear, tested, and maintainable code.
  • Experience with web services, data integrations, testing, logging, and basic monitoring across diverse data types.
  • Hands-on experience building with LLM tools or frameworks (prompting, structured outputs, tool-calling, retrieval, multi-step workflows) and awareness of common failure modes.
  • Experience evaluating LLM-powered applications: building test sets, reviewing failures, defining metrics, and iterating on prompts or retrieval.
  • Solid grounding in machine learning, statistics, and experimental design with ability to interpret technical papers and documentation.
  • Strong communication skills and comfort working with product, engineering, and business partners.
  • Interest in applying AI responsibly in financial services, including privacy, security, human review, and appropriate automation.
  • Familiarity with cloud deployment, containers, and modern release pipelines.

$140,000 - $160,000

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