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Scientific Machine Learning Jobs in Pennsylvania

As a Machine Learning Engineer in the Machine Intelligence Neural Design (MIND) team, you'll have ... Minimum Qualifications A PhD in computer science, computer engineering, or relevant Fields.

As a Machine Learning Engineer in the Machine Intelligence Neural Design (MIND) team, you'll have ... Minimum Qualifications A PhD in computer science, computer engineering, or relevant Fields.

New

As a Machine Learning Engineer in the Machine Intelligence Neural Design (MIND) team, you'll have ... Proficient in Swift, Objective-C, C++, or Go Minimum Qualifications A PhD in computer science ...

... science roles and advanced AI coursework. * Conceptual Teaching & Problem-Solving: Skilled at ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Machine Learning Tutor

Erie, PA · Remote

$18 - $40/hr

... science roles and advanced AI coursework. * Conceptual Teaching & Problem-Solving: Skilled at ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Senior Machine Learning Engineer

Pittsburgh, PA · On-site

$118K - $156K/yr

... scientific and analytical decisions. We're looking for world-class talent to join our team where you will have the opportunity to help develop a wide range of solutions that transform natural ...

Senior Machine Learning Engineer

Pittsburgh, PA · On-site

$118K - $156K/yr

... scientific and analytical decisions. We're looking for world-class talent to join our team where you will have the opportunity to help develop a wide range of solutions that transform natural ...

Machine Learning Tutor

Chester, PA · Remote

$18 - $40/hr

... science roles and advanced AI coursework. * Conceptual Teaching & Problem-Solving: Skilled at ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Machine Learning Tutor

Reading, PA · Remote

$18 - $40/hr

... science roles and advanced AI coursework. * Conceptual Teaching & Problem-Solving: Skilled at ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

... science roles and advanced AI coursework. * Conceptual Teaching & Problem-Solving: Skilled at ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

... science roles and advanced AI coursework. * Conceptual Teaching & Problem-Solving: Skilled at ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Showing results 21-40

Scientific Machine Learning information

What is scientific machine learning?

Scientific machine learning (SciML) is an interdisciplinary field that combines principles from machine learning and scientific computing to solve complex scientific and engineering problems. It involves developing algorithms and models that can learn from data and physical laws, such as differential equations, to make predictions, optimize systems, or gain insights into phenomena. SciML is widely used in areas like physics, biology, climate science, and engineering, enabling researchers to accelerate simulations and make data-driven discoveries. The field often leverages both traditional numerical methods and modern machine learning techniques, making it a rapidly evolving area of research.

What are some common challenges faced by professionals in scientific machine learning, and how can they be addressed?

Professionals in Scientific Machine Learning often encounter challenges such as integrating domain-specific scientific knowledge with machine learning models, managing large and complex datasets, and ensuring that models are interpretable and physically consistent. Collaboration with domain experts and interdisciplinary teams is essential to bridge knowledge gaps and validate results. To address these challenges, it is helpful to invest time in understanding the underlying scientific principles, keep up-to-date with advancements in both machine learning and scientific fields, and utilize specialized tools and frameworks designed for scientific data.

What are the key skills and qualifications needed to thrive as a scientific machine learning professional, and why are they important?

To thrive as a Scientific Machine Learning professional, you need a strong background in mathematics, statistics, programming (often Python), and domain-specific scientific knowledge, typically with a graduate degree in a STEM field. Proficiency in machine learning frameworks (such as TensorFlow or PyTorch), scientific computing tools (like NumPy, SciPy), and experience with high-performance computing are commonly required. Critical thinking, problem-solving, and collaborative communication are vital soft skills for designing experiments and interpreting complex data. These skills ensure robust, reproducible results and the ability to bridge scientific inquiry with advanced computational methods.

What is the difference between Scientific Machine Learning vs Data Scientist?

AspectScientific Machine LearningData Scientist
Required credentialsAdvanced degrees in CS, ML, or related fields; knowledge of scientific computingDegree in CS, statistics, or related fields; strong analytical skills
Work environmentResearch labs, academia, industry R&D teamsBusiness analytics, tech companies, consulting firms
Industry usageResearch, scientific computing, engineering simulationsBusiness insights, predictive modeling, data analysis

Scientific Machine Learning focuses on integrating scientific knowledge with machine learning techniques for research and engineering applications. Data Scientists analyze data to extract insights and build predictive models for business or operational purposes. While both roles require strong technical skills, Scientific Machine Learning emphasizes scientific computing and domain-specific modeling, whereas Data Scientists focus on data analysis and visualization.

What are popular job titles related to Scientific Machine Learning jobs in Pennsylvania?

For Scientific Machine Learning jobs in Pennsylvania, the most frequently searched job titles are:

What job categories do people searching Scientific Machine Learning jobs in Pennsylvania look for?

The top searched job categories for Scientific Machine Learning jobs in Pennsylvania are:

What cities in Pennsylvania are hiring for Scientific Machine Learning jobs?

Cities in Pennsylvania with the most Scientific Machine Learning job openings:

Infographic showing various Scientific Machine Learning job openings in Pennsylvania as of August 2026, with employment types broken down into 1% As Needed, 69% Full Time, 26% Part Time, 1% Temporary, 2% Contract, and 1% Nights. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Machine Learning Engineer

Apple

Pittsburgh, PA • On-site

Full-time

Posted 8 days ago


Apple rating

8.0

Company rating: 8.0 out of 10

Based on 677 frontline employees who took The Breakroom Quiz

7th of 30 rated technology retailers


Job description

As a Machine Learning Engineer in the Machine Intelligence Neural Design (MIND) team, you'll have the opportunity to be part of a leading ML innovation organization that develops a wide range of impactful Apple features. Within MIND, you'll engage in cutting-edge research in fields such as Foundation Models and Perception, and collaborate to create end products that have high impact on Apple users. This role places a strong emphasis on shipping ML-based features and products. You'll be involved in the entire end-to-end ML development pipeline, which encompasses creative approaches to dataset curation, model training, runtime inference integration, and on-device model optimizations. Our ideal team member is fearless in exploring new ideas and is willing to iterate on concepts. We value team members who can swiftly prototype and iterate, ultimately resulting in high-quality implementations.
Description
Our team seeks a self-driven machine learning engineer with strong experience in building ML training pipelines and developing production-quality inference infrastructure. In this role, you are expected to collaborate closely with ML researchers, SW/FW engineers, and Operation/Data engineers to advance different research and production efforts. Your role is to help deliver the needed pipeline for model development and evaluation, as well as build the production software to integrate these models and related functionality within Apple's software infrastructure. In addition, as part of the development process you are expected to build real-time demos and visualizations of sensing data streams and model predictions.
Minimum Qualifications
A PhD in computer science, computer engineering, or relevant Fields. Alternately, a BS or an MS + 3 to 5 years of ML engineering experience also qualifies.
Strong foundation in machine learning, and more specifically in LLM and multimodal foundation models.
Experience in building model training/eval pipelines in Python/PyTorch.
Experience in prototyping and developing software applications (preferably in Swift).
Experience with sensors and sensing systems.
Strong communication and presentation skills.
* Ability to work in a collaborative environment.
Preferred Qualifications
Proficient programming skills in Python and one of the deep learning toolkits such as JAX, PyTorch, or TensorFlow.
Experience in on-device ML model deployment and on-device optimization.
Experience handling multimodal data including text, images, audio, and other sensors.
Experience in developing production software.
Proficient in Swift, Objective-C, C++, or Go

What Apple employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom


Apple logo

About Apple

Sourced by ZipRecruiter

Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Cupertino, CA, US

Year founded

1976