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Machine Learning Part Time Jobs in Pittsburgh, PA

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Machine Learning Part Time information

See Pittsburgh, PA salary details

$24.8K

$41.3K

$85.4K

How much do machine learning part time jobs pay per year?

As of Aug 10, 2026, the average yearly pay for machine learning part time in Pittsburgh, PA is $41,341.00, according to ZipRecruiter salary data. Most workers in this role earn between $31,600.00 and $44,700.00 per year, depending on experience, location, and employer.

What is a machine learning part time job?

A Machine Learning Part Time job is a role where individuals work on ML-related tasks with a flexible or reduced schedule. These roles can involve data preprocessing, model development, evaluation, or deployment, depending on the organization's needs. Part-time positions are often suitable for students, freelancers, or professionals looking to gain experience while managing other commitments. They may be remote or on-site and can vary in duration and workload.

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

To thrive as a Machine Learning Part Time professional, you need a strong foundation in statistics, programming (often Python or R), and knowledge of core machine learning algorithms, typically demonstrated through a degree in computer science, engineering, or a related field. Familiarity with frameworks such as TensorFlow, Scikit-learn, or PyTorch and experience with data preprocessing tools or cloud platforms are commonly expected, and certifications like TensorFlow Developer can be beneficial. Effective communication, time management, and the ability to work independently are key soft skills for success in this role. These competencies enable you to efficiently contribute to projects, solve complex problems, and collaborate remotely or in hybrid team environments.

What are the typical responsibilities and expectations for a part-time machine learning role?

In a part-time machine learning position, you are generally expected to assist with data preprocessing, model development, and analysis of project results under the guidance of a senior data scientist or engineer. Your tasks might include cleaning datasets, coding algorithms, running experiments, and preparing reports or presentations for team meetings. The work is often project-based and requires regular communication with team members to ensure alignment on objectives and deliverables. This structure allows you to gain hands-on experience with real-world datasets and industry tools while maintaining a flexible schedule, making it ideal for students or professionals transitioning into the field.

What are the most commonly searched types of Machine Learning jobs in Pittsburgh, PA? The most popular types of Machine Learning jobs in Pittsburgh, PA are:
What are popular job titles related to Machine Learning Part Time jobs in Pittsburgh, PA? For Machine Learning Part Time jobs in Pittsburgh, PA, the most frequently searched job titles are:
What job categories do people searching Machine Learning Part Time jobs in Pittsburgh, PA look for? The top searched job categories for Machine Learning Part Time jobs in Pittsburgh, PA are:
What cities near Pittsburgh, PA are hiring for Machine Learning Part Time jobs? Cities near Pittsburgh, PA with the most Machine Learning Part Time job openings:
Machine Learning Part Time jobs near you
Infographic showing various Machine Learning Part Time job openings in Pittsburgh, PA as of August 2026, with employment types broken down into 1% As Needed, 72% Full Time, 24% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $41,341 per year, or $19.9 per hour.

Robotics Research Intern, Robot Learning (Fall 2026) | PhD Part-time Internship

FieldAI

Pittsburgh, PA • On-site

$55 - $59/hr

Full-time, Part-time, Internship

Re-posted 3 days ago

Job description

Field AI is transforming how robots interact with the real world. We are building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robotics, unlocking the full potential of embodied intelligence. We go beyond typical data-driven approaches or pure transformer-based architectures, and are charting a new course, with already-globally-deployed solutions delivering real-world results and rapidly improving models through real-field applications.
We are offering a Summer 2026 internship in Robot Learning for students interested in advancing embodied intelligence through large-scale learning, foundation models, and real-world robotic deployment. As a research intern, you will work closely with FieldAI researchers and engineers to explore novel approaches to robot learning and autonomy, with a focus on scalable methods that generalize across tasks and embodiments.
This internship is designed for PhD students who want to connect cutting-edge AI research with practical robotics systems. You will have the opportunity to design experiments, develop learning pipelines, and validate ideas on real robotic platforms, contributing directly to FieldAI's deployed autonomy stack.
What You Have:
  • Current PhD student in Robotics, Computer Science, Artificial Intelligence, Machine Learning, or a closely related field.
  • Research experience in robot learning, reinforcement learning, imitation learning, or related areas.
  • Strong foundation in machine learning fundamentals and experimental methodology.
  • Develop multi-modal data collection platform for day/night robot navigation data collection
  • Collect high-quality datasets for reproducible and comparable research and evaluation
  • Summarize and publish learnings in high-quality robot research conference or journal
  • Ability to work independently while collaborating effectively in a research environment.
  • Strong interest in embodied intelligence and real-world robotics systems.
The Extras That Set You Apart
  • Prior experience working with real robot platforms.
  • Familiarity with ROS or ROS 2.
  • Experience with large-scale or distributed training systems.
  • Publications or open-source contributions in robotics or AI.
  • Background in perception, planning, or control for robotics.
  • Interest in bridging foundational research with deployed robotic systems.

$55 - $59 an hour
What You Have:
  • Current PhD student in Robotics, Computer Science, Artificial Intelligence, Machine Learning, or a closely related field.
  • Research experience in robot learning, reinforcement learning, imitation learning, or related areas.
  • Strong foundation in machine learning fundamentals and experimental methodology.
  • Develop multi-modal data collection platform for day/night robot navigation data collection
  • Collect high-quality datasets for reproducible and comparable research and evaluation
  • Summarize and publish learnings in high-quality robot research conference or journal
  • Ability to work independently while collaborating effectively in a research environment.
  • Strong interest in embodied intelligence and real-world robotics systems.
The Extras That Set You Apart
  • Prior experience working with real robot platforms.
  • Familiarity with ROS or ROS 2.
  • Experience with large-scale or distributed training systems.
  • Publications or open-source contributions in robotics or AI.
  • Background in perception, planning, or control for robotics.
  • Interest in bridging foundational research with deployed robotic systems.

Our salary range is generous and we take into consideration an individual's background and experience in determining final salary; base pay offered may vary considerably depending on geographic location, job-related knowledge, skills, and experience.
Why Join Field AI?
We are solving one of the world's most complex challenges: deploying robots in unstructured, previously unknown environments. Our Field Foundational Models™ set a new standard in perception, planning, localization, and manipulation, ensuring our approach is explainable and safe for deployment.
You will have the opportunity to work with a world-class team that thrives on creativity, resilience, and bold thinking. With a decade-long track record of deploying solutions in the field, winning DARPA challenge segments, and bringing expertise from organizations like DeepMind, NASA JPL, Boston Dynamics, NVIDIA, Amazon, Tesla Autopilot, Cruise Self-Driving, Zoox, Toyota Research Institute, and SpaceX, we are set to achieve our ambitious goals.
Be Part of the Next Robotics Revolution
To tackle such ambitious challenges, we need a team as unique as our vision - innovators who go beyond conventional methods and are eager to tackle tough, uncharted questions. We're seeking individuals who challenge the status quo, dive into uncharted territory, and bring interdisciplinary expertise. Our team requires not only top AI talent but also exceptional software developers, engineers, product designers, field deployment experts, and communicators.
We are headquartered in always-sunny Irvine, Southern California and have US based and global teammates.
Join us, shape the future, and be part of a fun, close-knit team on an exciting journey!
We celebrate diversity and are committed to creating an inclusive environment for all employees. Candidates and employees are always evaluated based on merit, qualifications, and performance. We will never discriminate on the basis of race, color, gender, national origin, ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, mental or physical disability, or any other legally protected status.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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