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Machine Learning Engineer Part Time Jobs in New York

Tower Research Capital

Quantitative Trader

New York, NY · On-site

$125K - $250K/yr

... machine learning. Our ongoing investment in top engineering talent and technology ensures our ... Develop, augment and calibrate exchange simulators * Part-time work from home benefits ...

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Machine Learning Engineer Part Time information

How do part-time Machine Learning Engineers typically balance project ownership with limited working hours?

Part-time Machine Learning Engineers often focus on well-defined project segments, collaborating closely with full-time team members to ensure alignment and continuity. Clear communication, thorough documentation, and regular check-ins are key to maintaining progress and integrating their contributions seamlessly. While they may not own entire projects, they often take responsibility for specific modules, models, or experiments, and their schedules are usually coordinated to overlap with team meetings or sprints. This structure allows part-time engineers to add significant value while maintaining a manageable workload.

What is the difference between Machine Learning Engineer Part Time vs Data Scientist Part Time?

AspectMachine Learning Engineer Part TimeData Scientist Part Time
Required CredentialsBachelor's or Master's in CS, AI, or related; experience with ML frameworksBachelor's or Master's in CS, Statistics, or related; experience with data analysis
Work EnvironmentTech companies, startups, research labs; project-basedBusiness, finance, healthcare; data analysis and reporting
Employer & Industry UsageTech firms, AI startups, R&D departmentsCorporate sectors, consulting firms, research institutions

Machine Learning Engineer Part Time focuses on developing and deploying ML models, while Data Scientist Part Time emphasizes analyzing data to extract insights. Both roles often require similar educational backgrounds and may work in overlapping industries, but their core responsibilities differ. Understanding these distinctions helps job seekers target the right position based on their skills and career goals.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer Part Time, and why are they important?

To thrive as a Machine Learning Engineer Part Time, you need strong programming skills (especially in Python), a solid understanding of machine learning algorithms, and ideally a degree in computer science or a related field. Familiarity with tools such as TensorFlow, PyTorch, and cloud platforms, as well as experience with version control systems like Git, is typically required. Excellent problem-solving abilities, adaptability, and clear communication are valuable soft skills for collaborating on projects and conveying technical concepts. These skills ensure effective development, deployment, and optimization of machine learning models within the constraints of a part-time role.

What is a Machine Learning Engineer (Part Time)?

A Machine Learning Engineer (Part Time) is a professional who designs, builds, and implements machine learning models and algorithms, but works fewer hours than a full-time employee—often on a flexible or project-based schedule. These engineers collaborate with data scientists and software developers to integrate intelligent systems into products or services. Part-time roles are ideal for those seeking work-life balance, students, or professionals supplementing their income. Responsibilities may include data preprocessing, model training, and deployment, but the scope is typically tailored to fit part-time hours.
What are the most commonly searched types of Machine Learning Engineer jobs in New York? The most popular types of Machine Learning Engineer jobs in New York are:
What cities in New York are hiring for Machine Learning Engineer Part Time jobs? Cities in New York with the most Machine Learning Engineer Part Time job openings:
Machine Learning Engineer Part Time jobs near you
Adjunct Associate Faculty, Applied Generative AI (On-Campus, Fall '26)

Adjunct Associate Faculty, Applied Generative AI (On-Campus, Fall '26)

Columbia University

New York, NY • On-site

$2.0K - $3.0K/wk

Part-time

Posted 27 days ago

Job description

Company Description
Columbia University has been a leader in higher education in the nation and around the world for more than 250 years. At the core of our wide range of academic inquiry is the commitment to attract and engage the best minds to pursue greater human understanding, pioneering discoveries, and service to society.
The School of Professional Studies at Columbia University offers innovative and rigorous programs that integrate knowledge across disciplinary boundaries, combine theory with practice, leverage the expertise of our students and faculty, and connect global constituencies. Through twenty professional master's degrees, courses for advancement and graduate school preparation, certificate programs, summer courses, high school programs, and a program for learning English as a second language, the School of Professional Studies transforms knowledge and understanding in service of the greater good.
Job Description
Seeking analytics professionals to serve as a part-time Associate for a graduate-level course on Applied Generative AI. An Associate is a faculty line junior to a Lecturer, that provides subject matter expertise and supports the instructional process for a course section. Serving as an Associate is an outstanding way to gain exposure to graduate-level teaching at Columbia University.
The Applied Generative AI course provides students with a comprehensive introduction to a branch of machine learning called generative modeling, focusing on the underlying concepts, theoretical techniques, and practical applications. Students will learn to use, fine-tune, and programmatically interface with high-level APIs and open-source foundational models, allowing them to leverage state-of-the-art tools in Generative AI. Additionally, the course delves into the theory and practice of low-level implementations, empowering students to train their own models on their own data and understand these models from first principles. The course covers various types of generative models, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Transformers with their applications to text, image, audio, and video generation.
Responsibilities
  • Attend all class sessions, assist with instruction, lead breakout sessions, facilitate discussions.
  • Evaluate, grade student work and assessments as requested by the course Lecturer.
  • Monitor and address student concerns and inquiries.

Qualifications
Columbia University SPS operates under a scholar-practitioner faculty model, which enables students to learn from faculty possessing outstanding academic training as well as a record of accomplishment as practitioners in an applied industry setting.
Requirements
  • Graduate degree in an area related to Machine Learning, Computer Science, Applied Mathematics, or related field.
  • 3+ years of related applied professional experience.

Preferred Skills & Experience
  • Programming experience in Python and experience with major deep learning frameworks such as PyTorch or TensorFlow.
  • Knowledge of deep learning architectures, such as CNNs, VAEs, GANs, and RNNs.
  • Experience with deploying code on cloud platforms such as AWS, GCP, or Azure.
  • Knowledge of Mathematics and Probability concepts used in machine learning, including
  • Optimization, Gradient Descent, Conditional Probability, Bayes Theorem, and Normal Distribution.

Additional Information
Salary range: $2,000 - $3,000 per semester long course
Please submit a resume inclusive of university teaching experience.
All your information will be kept confidential according to EEO guidelines.
Columbia University is an Equal Opportunity Employer / Disability / Veteran

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