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Scientific Machine Learning Jobs in Pennsylvania

Air

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 ...

Varsity Tutors

Machine Learning Tutor

State College, 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 ...

BE Group

Senior Machine Learning Engineer

Malvern, PA

$120K - $158K/yr

We are assisting our client in hiring for a Senior Machine Learning Engineer. Our client is an ... Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Mathematics, or a ...

The Pennsylvania State University

Strongproficiencyin Python for scientific computing and machine learning, including experience with common ML libraries/frameworks (e.g.,PyTorch, TensorFlow, JAX, scikit-learn). * Demonstrated ...

Vanguard Group

Senior Machine Learning Engineer

Malvern, PA · On-site

$102K - $140K/yr

Partner with quantitative researchers and data scientists to productionalize research models ... Experience in software engineering, machine learning engineering, data engineering, or a related ...

Vangard, Inc.

Senior Machine Learning Engineer

Malvern, PA

$102K - $140K/yr

Partner with quantitative researchers and data scientists toproductionalizeresearch models ... Experience in software engineering, machine learning engineering, data engineering, or a related ...

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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:

Scientific Machine Learning jobs near you
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.

Senior Machine Learning Engineer

Air

Pittsburgh, PA • On-site

$118K - $156K/yr

Full-time

Re-posted 28 days ago

Job description

Company Description
Air is the leader in Enterprise Readiness. Our mission is to establish readiness as a real-time condition that is continuously achieved. Today, a dangerous Readiness Gap exists between what the front line needs and what is delivered. Our AI-native platform, Air Enterprise Readiness, aligns development, production, delivery, and sustainment into one coordinated execution system for government agencies and industrial suppliers. By revealing true capacity, exposing real constraints, coordinating resources, and executing at the speed of operational demands, the front line gets what it needs to succeed.
Job Description
We are seeking a Natural Language Processing expert to join our team and help us build cutting-edge machine learning technology that will replace complex, time-consuming, manual processes with automation and intelligence that helps Air end-users make 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 language data into useful features for classification algorithms, as well as implement the latest technology to improve user search functionality. 
 
In order to do this job well, you must be a curious and eager problem solver with a hunger for building well-designed models and solving problems in an ambiguous space. You share our intolerance of mediocrity. You're uber-smart, challenged by figuring things out and producing simple solutions to complex problems. Knowing there are always multiple answers to a problem, you know how to engage in a constructive dialogue to find the best path forward. You're scrappy. We like scrappy. 
 
This role is a full-time position located out of our office in Pittsburgh, PA. 
 
This role may require up to 10% travel
Scope of Responsibilities
  • Inform and implement the design and development of NLP applications to enhance the intelligence and efficiency of our data analytics software-as-a-service platform
  • Review, verify, and aggregate the most appropriate annotated datasets for the best-supervised learning methods
  • Ability to work with taxonomy experts to create and validate dataset annotation
  • Use well-formed and effective text representation to change and adapt natural language documents into user-friendly features
  • implement the latest technology to improve user search functionality as well as integrate state of the art LLM models into our Air Enterprise Readiness platform to enhance user experience.
  • Develop new algorithms and modeling techniques and conduct sound experiments to verify model results and integrate models into the live production system
  • Establish meaningful criteria for evaluating algorithm performance and suitability
  • Implement working, scalable, production-ready models and code
  • Keep up to date with Machine Learning best practices and evolving open-source frameworks
  • Regularly seek out innovation and continuous improvement, finding efficiency in all assigned tasks
  • Collaborate closely with fellow taxonomists, software engineers, data scientists, data engineers, and QA engineers
Qualifications
  • U.S. Citizenship is required
  • Advanced degree, or bachelor's with at least 3 years of experience, in Data Science, Machine Learning or a related field
Required Skills: 
  • Minimum 3 years experience with hands-on development of NLP models
  • In-depth understanding of NLP methods for text representation, semantic extraction techniques, data structures, and modeling
  • Practical experience in building, developing, and productionizing both supervised and unsupervised machine learning models
  • Advanced software skills in Python
  • Advanced ability in forming SQL queries
  • A strong desire to learn, investigate, and implement cutting-edge technologies
  • Ability to work collaboratively throughout the design process.

Desired Skills: 

  • Current possession of a U.S. security clearance, or the ability to obtain one with our sponsorship
  • Experience in or exposure to the nuances of a startup or other entrepreneurial environment
  • Experience in deploying ML models in Kubernetes environments
  • Experience developing custom training sets for large language models
  • Experience productionizing transformer-based models
We firmly believe that past performance is the best indicator of future performance.  If you thrive while building solutions to complex problems, are a self-starter, and are passionate about making an impact in global security, we're eager to hear from you.
 
Air is an Equal Opportunity Employer.  All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans status or any other characteristic protected by law.

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