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Physics Informed Machine Learning Jobs in Marlton, NJ

USCS

Data Scientist

Camden, NJ · On-site

$105K - $130K/yr

Master's degree in mathematics, statistics, physics, computer science or related field * 2-4 years of experience in applied data science, machine learning, or advanced analytics roles * Strong ...

United States Cold Storage

Data Scientist

Camden, NJ · On-site

$105K - $130K/hr

Master's degree in mathematics, statistics, physics, computer science or related field * 2-4 years of experience in applied data science, machine learning, or advanced analytics roles * Strong ...

United States Cold Storage

Data Scientist

Camden, NJ · On-site

$105K - $130K/yr

Master's degree in mathematics, statistics, physics, computer science or related field * 2-4 years of experience in applied data science, machine learning, or advanced analytics roles * Strong ...

United States Cold Storage

Data Scientist

Camden, NJ · On-site

$105K - $130K/hr

Master's degree in mathematics, statistics, physics, computer science or related field * 2-4 years of experience in applied data science, machine learning, or advanced analytics roles * Strong ...

Pwc

AI Engineer

Philadelphia, PA · On-site

$50K - $112K/yr

... informed decision-making and driving business growth. Within our Internal Firm Services practice ... Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS ...

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How much do physics informed machine learning jobs pay per hour?

As of Sep 12, 2026, the average hourly pay for physics informed machine learning in Marlton, NJ is $20.42, according to ZipRecruiter salary data. Most workers in this role earn between $12.74 and $25.91 per hour, depending on experience, location, and employer.

What is a physics informed machine learning?

A Physics Informed Machine Learning (PIML) job involves developing AI models that integrate physics-based principles to improve accuracy, interpretability, and generalization. Professionals in this role use machine learning techniques alongside domain knowledge in physics, engineering, or applied sciences to solve complex problems in areas like fluid dynamics, materials science, and climate modeling. Responsibilities often include designing algorithms, implementing simulations, and validating results against experimental or real-world data. Employers typically seek expertise in deep learning, numerical methods, and programming languages like Python.

What are the typical challenges faced by professionals working in physics informed machine learning roles?

Professionals in Physics Informed Machine Learning often encounter challenges integrating complex physical theories with advanced machine learning models, requiring deep domain knowledge and strong technical skills. Balancing model accuracy with computational efficiency and ensuring that models are both interpretable and generalizable can be demanding. Collaboration with domain experts, data scientists, and engineers is common, as projects often span multiple disciplines. Successfully navigating these challenges provides valuable experience and is highly regarded, often leading to further career advancement in research, engineering, or leadership positions.

What are the key skills and qualifications needed to thrive in the physics informed machine learning position, and why are they important?

To thrive in Physics Informed Machine Learning, you need a solid background in physics, strong mathematical and statistical skills, and experience with machine learning algorithms, typically supported by an advanced degree in a relevant field. Proficiency with programming languages like Python, frameworks such as TensorFlow or PyTorch, and familiarity with numerical simulation tools are commonly required. Effective problem-solving, clear communication, and the ability to collaborate with interdisciplinary teams make a significant impact in this role. These capabilities are essential for developing robust, interpretable machine learning models that leverage physical laws to solve complex, real-world problems.

What cities near Marlton, NJ are hiring for Physics Informed Machine Learning jobs?

Cities near Marlton, NJ with the most Physics Informed Machine Learning job openings:

Physics Informed Machine Learning jobs near you
Infographic showing various Physics Informed Machine Learning job openings in Marlton, NJ as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $42,466 per year, or $20.4 per hour.

Quantitative Researcher - Master's: 2027

Philadelphia, PA • On-site

Susquehanna International Group, LLP
Finance and Insurance • 1 - 5K employees
Learn more about Susquehanna International Group, LLPexternal

Full-time

Re-posted 15 days ago

Job description

Overview
At Susquehanna, quantitative researchers tackle challenging data and algorithmic problems that inform decision-making in global financial markets. Their work spans statistical inference, optimization, market microstructure, simulation, forecasting, and large-scale data analysis, drawing on a broad quantitative toolkit to develop new insights and approaches.
As a Quantitative Researcher, you'll blend strong research capabilities with a deep understanding of trading to design, validate, backtest, and implement statistical and advanced machine learning models. Your work will span large-scale data analysis, alpha signal research, and strategy performance enhancement. While there is some overlap with the Quantitative Systematic Trader role, quantitative researchers typically focus more on model development, robustness, and long-term reliability.
You'll have access to Susquehanna's extensive proprietary datasets and large-scale computing infrastructure-including a rapidly growing cluster of thousands of high-end GPUs-support computationally intensive research, large-scale simulation, and rapid experimentation. Researchers work in small, highly collaborative teams where ideas are debated openly, evidence guides decisions, and promising research can quickly produce practical impact.
What You Can Expect
  • Modelling: Apply probability theory, statistical analysis, and machine learning techniques to build robust models and generate alphas
  • Execution: Propose improvements to existing strategies and optimize how they perform in production
  • Evaluation: Backtest ideas using historical market data and large-scale research clusters
  • Education: Participate in a comprehensive education program and receive personalized mentorship from senior professionals to accelerate your growth
  • Collaboration: Work in an open environment where you'll collaborate with systematic traders and technologists to push strategies into production

What we're looking for
We're looking for intellectually curious researchers who are energized by difficult quantitative problems and motivated by rigorous experimentation.
You may be a strong fit if you have:
  • A master's degree, graduating by Summer 2027, in computer science, economics, electrical engineering, mathematics, operations research, physics, statistics, or another highly quantitative discipline
  • A strong foundation in probability, statistics, optimization, numerical methods, or quantitative modeling
  • Experience developing research code in Python and working with large datasets; experience with C++, scientific computing, or high-performance computing is a plus
  • Excellent problem-solving skills and an interest in using quantitative models to better understand complex systems
  • Strong communication skills and an interest in collaborating with researchers, engineers, and traders to refine ideas through discussion and experimentation
  • A track record of intellectual curiosity demonstrated through coursework, independent projects, research, competitions, strategic games, or other analytical pursuits
  • The ability to thrive in an environment that values creativity, evidence-based thinking, and continuous learning

Visa sponsorship is available for this position.
By applying to this role, you will be automatically considered for the Quantitative Systematic Trader position. There is no need to apply to both positions to be considered for both.
About Susquehanna
Susquehanna is a global quantitative trading firm powered by scientific rigor, curiosity, and innovation. Our culture is intellectually driven and highly collaborative, bringing together researchers, engineers, and traders to design and deploy impactful strategies in our systematic trading environment. To meet the unique challenges of global markets, Susquehanna applies machine learning and advanced quantitative research to vast datasets in order to uncover actionable insights and build effective strategies. By uniting deep market expertise with cutting-edge technology, we excel in solving complex problems and pushing boundaries together.
If you're a recruiting agency and want to partner with us, please reach out to recruiting@sig.com. Any resume or referral submitted in the absence of a signed agreement will not be eligible for an agency fee.
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