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Scientific Machine Learning Jobs in Berkeley Heights, NJ

Point72

Machine Learning Engineer

New York, NY

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 ...

Oxman

Machine Learning Research Engineer

New York, NY · On-site

$142K - $184K/yr

Working across disciplines-from architecture and ecology to materials science and computation, we ... Develop machine learning models for geospatial inference of key ecosystem metrics, leveraging ...

Spgi

Machine Learning Engineer

New York, NY · On-site +1

$209K - $250K/yr

Job Requirements: Master's degree in Computer Science, Statistics, Data Science, or related ... Machine Learning (ML) and artificial intelligence (Al) tools Data Preprocessing, Exploration and ...

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How much do scientific machine learning jobs pay per hour?

As of Aug 13, 2026, the average hourly pay for scientific machine learning in Berkeley Heights, NJ is $32.85, according to ZipRecruiter salary data. Most workers in this role earn between $20.05 and $41.88 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 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 Berkeley Heights, NJ?

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

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

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

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

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

Scientific Machine Learning jobs near you
Infographic showing various Scientific Machine Learning job openings in Berkeley Heights, NJ as of August 2026, with employment types broken down into 1% As Needed, 72% Full Time, 23% Part Time, 1% Temporary, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $68,331 per year, or $32.9 per hour.

Machine Learning Engineer

Point72

New York, NY

Full-time

Re-posted yesterday

Job description

About Cubist

Cubist Systematic Strategies, an affiliate of Point72, deploys systematic, computer-driven trading strategies across multiple liquid asset classes, including equities, futures and foreign exchange. The core of our effort is rigorous research into a wide range of market anomalies, fueled by our unparalleled access to a wide range of publicly available data sources.

Role/Responsibilities:

We are seeking a Machine Learning Engineer to join the High Frequency Trading Technology team.

This role will apply the latest AI technologies to solve various real-world problems and streamline day-to-day operations, such as creating a production support AI agent that helps monitor production problems and suggest actions.

This role will also work with the AI research group on various projects such as creating synthetic data for training and using MCP agents to streamline research workflow.

Requirements:

  • 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 deep neural networks and representation learning
  • Prior experience working in a data driven research environment
  • Experience with translating mathematical models and algorithms into code
  • Proficiency in programming languages such as Python and R
  • Experience with machine learning software libraries such as TensorFlow or PyTorch
  • Experience implementing Agent or Context engineering is strongly preferred
  • Experience with natural language processing technology is strongly preferred
  • Excellent analytical skills, with strong attention to detail
  • Collaborative mindset with strong independent research ability
  • Strong written and verbal communication skills
  • Commitment to the highest ethical standards

Apply