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Scientific Machine Learning Jobs in New Jersey (NOW HIRING)

Workiva

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

Workiva

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

Workiva

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

Workiva

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

Workiva

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

Workiva

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Your primary role is to contribute to Workiva's data scientists' efforts within the Data Management ...

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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 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 New Jersey?

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

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

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

What cities in New Jersey are hiring for Scientific Machine Learning jobs?

Cities in New Jersey with the most Scientific Machine Learning job openings:

Scientific Machine Learning jobs near you
Infographic showing various Scientific Machine Learning job openings in New Jersey as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 20% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Machine Learning Engineer

5 Star Global Recruitment Partners

Newark, NJ • On-site

Full-time

This job post has expired today. Applications are no longer accepted.

Job description

Machine Learning Engineer

Newark, New Jersey, United States

Job Description

As a Machine Learning Engineer, you will play a pivotal role in driving the development and implementation of cutting-edge machine learning solutions for our client. Your responsibilities will encompass a wide range of tasks, from leading a small team of machine learning engineers to collaborating with cross-functional teams to deliver impactful solutions. You will be at the forefront of driving innovation and leveraging the power of machine learning to solve real-world problems, drive business growth, and create value.

Key Responsibilities

  • Lead and drive machine learning projects from inception to production: build relationships with business partners and cross-functional teams.
  • Collaborate with business leaders, subject matter experts, and decision-makers to develop success criteria and optimize new products, features, policies, and models.
  • Partner with data scientists to understand, implement, train, and design machine learning models.
  • Collaborate with the infrastructure team to improve the architecture, scalability, stability, and performance of ML platform.
  • Construct optimized data pipelines to feed machine learning models.
  • Extend existing machine learning libraries and frameworks.
  • Develop processes, model monitoring, and governance framework for successful ML model operationalization.
  • Define objectives for the Machine Learning platform, own the technical roadmap, and be accountable for delivering results.
  • Define standards for engineering and operational excellence for running best-in-class ML platforms and continue to improve ML platforms to keep up with the latest innovations.
  • Design and implement the best architectural practices in the delivery of data science use cases.

Key Skills/Knowledge/Experience

  • 7+ years of experience in Machine Learning.
  • Extensive software engineering experience with strong working experience as a Machine Learning Engineer.
  • Bachelor's degree in computer science, computer engineering, or a related engineering field. Masters degree preferred.
  • Advanced proficiency with Python, Java, and Scala.
  • Strong computer science fundamentals such as algorithms, data structures, multithreading.
  • Experience working with Generative AI, using LangChain for Gen AI and techniques like RAG.
  • Experience using ML and DL Libraries:XGBoost, SKlearn, Tensorflow or PyTorch
  • In-depth experience building solutions using public clouds such as AWS, GCP.
  • Experience using ML platforms like SageMaker, H2O, DataRobot, etc.
  • Strong knowledge on ML model development life cycle components like containers, batch vs real time inference endpoints, application security testing etc.
  • Experience managing relationships in a cross-functional environment with multiple stakeholders.
  • Experience with developing and deploying production-grade applications with ML inferences using automation pipeline on cloud.
  • Experience working in Agile/ Scrum development process.
  • Thought leadership and innovative thinking.
  • Excellent communication and collaboration skills.

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