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Scientific Machine Learning Jobs in Newark, NJ (NOW HIRING)

Senior Machine Learning Engineer

Manhattan, NY · On-site

$133K - $176K/yr

A degree in Computer Science, Machine Learning, or a related field, or equivalent professional experience. * A strong theoretical foundation in core Machine Learning concepts and techniques.

Senior Machine Learning Engineer

Manhattan, NY · On-site

$133K - $176K/yr

A degree in Computer Science, Machine Learning, or a related field, or equivalent professional experience. * A strong theoretical foundation in core Machine Learning concepts and techniques.

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 ...

PhD or PhD candidate in machine learning, computer science or other AI related research fields * Experience with sequential modeling and time series forecasting using deep learning * Experience with ...

PhD or PhD candidate in machine learning, computer science or other AI related research fields * Experience with sequential modeling and time series forecasting using deep learning * Experience with ...

PhD or PhD candidate in machine learning, computer science or other AI related research fields * Experience with sequential modeling and time series forecasting using deep learning * Experience with ...

We are seeking a highly adaptable, creative, and well‑rounded Machine Learning Engineer to join ... Bachelor's or Master's degree in Computer Science, AI, Data Science, or a related technical field.

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Scientific Machine Learning information

See Newark, NJ salary details

$14

$32

$54

How much do scientific machine learning jobs pay per hour?

As of Aug 19, 2026, the average hourly pay for scientific machine learning in Newark, NJ is $32.92, according to ZipRecruiter salary data. Most workers in this role earn between $20.10 and $41.97 per hour, depending on experience, location, and employer.

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 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 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 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 Newark, NJ?

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

What job categories do people searching Scientific Machine Learning jobs in Newark, NJ look for?

The top searched job categories for Scientific Machine Learning jobs in Newark, NJ are:

What cities near Newark, NJ are hiring for Scientific Machine Learning jobs?

Cities near Newark, NJ with the most Scientific Machine Learning job openings:

Machine Learning Research Scientist

Strivector

Manhattan, NY • On-site

Other

Posted 13 days ago


Job description

Machine Learning Research ScientistIntroduction:

We are seeking a Machine Learning Research Scientist capable of independently tackling research problems with commercial applications. The ideal candidate will apply technical expertise and research acumen to real-world financial and operating problems in our New York, NY office.

Responsibilities:
  • Conduct research on machine learning algorithms and their applications in finance
  • Develop models and tools for analyzing large datasets and extracting insights
  • Collaborate with cross-functional teams to implement machine learning solutions
  • Publish research findings in academic journals and present at conferences
  • Stay current on the latest advancements in machine learning and finance
Requirements:
  • Development experience in Python or R (C, C++, Java, etc is a plus)
  • Machine Learning experience
  • Deep understanding of statistical learning methods
  • Strong communications and organizational skills
  • 4+ years of applicable research experience
  • Bachelor''s degree required, Ph.D. desired