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Machine Learning Engineer Data Science Intern Jobs in New York, NY

Forhyre

Machine Learning Engineer

New York, NY

If you also have knowledge of data science and software engineering, we'd like to meet you. Your ... Design machine learning systems * Research and implement appropriate ML algorithms and tools

Aston Carter

Machine Learning Engineer

Manhattan, NY · On-site

$125/hr

Essential Skills * 5+ years of professional experience in Data Science, Machine Learning, Advanced Analytics, Quantitative Research, Data Engineering, or related fields. * Strong proficiency in ...

PhysicsX

Machine Learning Engineer

New York, NY · On-site

Working at the intersection of data science and software engineering, you translate R&D and project ... This Role As a Machine Learning Engineer, you'll work closely with our Data Scientists, Simulation ...

Hang

Machine Learning Engineer

New York, NY · On-site +1

This person will implement and develop machine learning models to enhance our platform ... Work closely with software engineers, data scientists, and product managers to integrate ML models ...

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All JobsMachine Learning Engineer Data Science Intern JobsMachine Learning Engineer Data Science Intern Jobs in New York, NY

Machine Learning Engineer Data Science Intern information

See New York, NY salary details

$27.9K

$46.6K

$96.3K

How much do machine learning engineer data science intern jobs pay per year?

As of Sep 9, 2026, the average yearly pay for machine learning engineer data science intern in New York, NY is $46,588.00, according to ZipRecruiter salary data. Most workers in this role earn between $35,600.00 and $50,300.00 per year, depending on experience, location, and employer.

What does a machine learning engineer data science intern do?

A Machine Learning Engineer Data Science Intern assists in developing and implementing machine learning models and data-driven solutions under the guidance of experienced professionals. The role typically involves data preprocessing, feature engineering, model training, and evaluation. Interns often collaborate with teams to solve real-world problems using statistical analysis and programming. They gain hands-on experience with popular tools and frameworks, such as Python, TensorFlow, and scikit-learn. This internship helps build foundational skills for a future career in machine learning and data science.

What kinds of projects does a machine learning engineer data science intern typically work on, and how are they supported by their team?

As a Machine Learning Engineer Data Science Intern, you will often be assigned to real-world projects such as building predictive models, cleaning and preprocessing data, and assisting with the deployment of machine learning solutions. You’ll collaborate closely with senior data scientists, software engineers, and sometimes product managers, receiving guidance during code reviews and regular team meetings. The environment is typically fast-paced and supportive, encouraging learning through mentorship and hands-on experience. Interns are expected to communicate their findings clearly and contribute to the team’s problem-solving efforts.

What are the key skills and qualifications needed to thrive as a machine learning engineer data science intern, and why are they important?

To thrive as a Machine Learning Engineer Data Science Intern, you need a strong background in mathematics, statistics, and programming (typically Python), supported by coursework or a degree in computer science, data science, or a related field. Familiarity with tools and libraries like TensorFlow, PyTorch, scikit-learn, and version control systems such as Git is commonly required. Problem-solving skills, intellectual curiosity, and effective communication make a candidate stand out in collaborative and dynamic environments. These skills are vital for analyzing complex datasets, building reliable models, and clearly communicating insights that drive data-driven decision-making.

Senior Data Science and Machine Learning Engineer

New York, NY • Remote

1 point system
IT Services • 51 - 200 employees
Learn more about 1 point systemexternal

Contractor

Re-posted 28 days ago

Job description

Job Summary:

  • We are seeking a Senior Data Science Engineer to design, build, and scale data-driven systems that power advanced analytics and machine learning across our organization. This role sits at the intersection of software engineering and data science; you’ll be responsible for building robust data pipelines, enabling experimentation, and deploying production-ready machine learning models.
  • As a senior team member, you will mentor junior engineers and data scientists, influence architectural decisions, and help shape the long-term AI and data strategy.

Key Responsibilities:

  • Develop, deploy, and maintain machine learning models in production environments.
  • Collaborate with data scientists, analysts, and product managers to define and deliver data-driven features.
  • Ensure high-quality data through monitoring, validation, and robust testing frameworks.
  • Architect and maintain data platforms and tools for experimentation, model serving, and feature engineering.
  • Explore and integrate Large Language Models (LLMs) and other generative AI approaches into business applications and data workflows.
  • Contribute to code reviews, technical design discussions, and best practices for the team.
  • Mentor and guide junior engineers/data scientists, fostering technical excellence and career growth.
  • Stay current with emerging technologies in Data Science, Machine Learning, LLM Ops, ML Ops.

Education Requirement:

  • Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
  • Master’s degree or PhD is a strong plus.

Experience:

  • 5+ years of experience in data engineering, machine learning engineering, or related roles.
  • Strong proficiency in Python (Pandas, NumPy, PySpark, or similar).
  • Solid understanding of ML model development, training, and deployment pipelines.
  • Experience with ML model monitoring and observability frameworks.
  • Experience with deep learning frameworks(TensorFlow, PyTorch).
  • Familiarity with CI/CD, version control (Git),and modern ML Ops practices.