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Physics Informed Machine Learning Jobs in Hoboken, NJ

ICF

Senior Climate Analytics Specialist

New York, NY · On-site

$95K - $117K/yr

Translate complex climate risk questions into tractable analytical and modeling frameworks, selecting appropriate statistical, machine learning, and physics-informed approaches * Design, train, and ...

Thalo Labs

Senior Machine Learning Engineer

New York, NY · On-site

$150K - $180K/yr

We're building the physical AI layer for HVAC: sensors plus physics-based intelligence that turn ... As our Senior Machine Learning Engineer, you'll own the intelligence layer of Thalo's platform. We ...

Thalo Labs

Senior Machine Learning Engineer

New York, NY · On-site

$150K - $180K/yr

We're building the physical AI layer for HVAC: sensors plus physics-based intelligence that turn ... As our Senior Machine Learning Engineer, you'll own the intelligence layer of Thalo's platform. We ...

Thalo Labs

Senior Machine Learning Engineer

New York, NY

$150K - $180K/yr

We're building the physical AI layer for HVAC: sensors plus physics-based intelligence that turn ... As our Senior Machine Learning Engineer, you'll own the intelligence layer of Thalo's platform. We ...

PhysicsX

Senior Machine Learning Engineer

New York, NY · On-site

$114K - $157K/yr

By enabling high-fidelity, multi-physics simulation through AI inference across the entire ... Who We're Looking For As a Senior Machine Learning Engineer in Delivery, you are an experienced ...

Applied Physics

Data Scientist

New York, NY · On-site

Applied Physics is seeking a Data Scientist experienced with a diverse array of data types to join ... analysis, machine learning, information visualization, as well as others. Responsibilities:

Applied Physics

Data Scientist

New York, NY · On-site

Applied Physics is seeking a Data Scientist experienced with a diverse array of data types to join ... analysis, machine learning, information visualization, as well as others. Responsibilities:

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

As of Aug 20, 2026, the average hourly pay for physics informed machine learning in Hoboken, NJ is $21.95, according to ZipRecruiter salary data. Most workers in this role earn between $13.65 and $27.88 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.

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Physics Informed Machine Learning jobs near you

Machine Learning Researcher - PhD: 2027

SIG Susquehanna

Manhattan, NY • On-site

$270 - $330/hr

Other

Posted 2 days ago

New

Job description

Overview

Susquehanna is expanding the Machine Learning group and seeking exceptional researchers to join our dynamic team. As a Machine Learning Researcher, you will apply advanced ML techniques to a wide range of forecasting challenges, including time series analysis, natural language understanding, and more. Your work will directly influence our trading strategies and decision-making processes. This is a unique opportunity to work at the intersection of cutting-edge research and real-world impact, leveraging one of the highest-quality financial datasets in the industry.


We’re looking for research scientists with a proven track record of applying deep learning to solve complex, high-impact problems. The ideal candidate will have a strong grasp of diverse machine learning techniques and a passion for experimenting with model architectures, feature engineering, and hyperparameter tuning to produce resilient and high-performing models.


What you\'ll do

  • Research and develop deep learning models to generate and enhance systematic trading signals and strategies across asset classes.

  • Collaborate closely with researchers, traders, and developers to improve alpha generation and identify new algorithmic trading strategies.

  • Design and conduct rigorous experiments using modern machine learning frameworks to improve predictive signals and overall trading performance.

  • Apply scientific methods to extract actionable signals from complex datasets, deepening the understanding of market behavior.

  • Translate research insights into production-ready models that can be implemented, tested, and validated in live trading environments.

  • Partner with engineering and trading teams to deploy, monitor, and iterate on models that drive trading decisions and execution outcomes.


What we\'re looking for

  • PhD in computer science, machine learning, mathematics, physics, statistics, or a related field

  • Strong track record of applying ML in academic or industry settings, with 5+ years of experience building impactful deep learning systems

  • A strong publication record in top-tier conferences such as NeurIPS, ICML, or ICLR

  • Strong programming skills in Python and/or C++

  • Practical knowledge of ML libraries and frameworks, such as PyTorch or TensorFlow, especially in production environments

  • Hands-on experience applying deep learning on time series data

  • Strong foundation in mathematics, statistics, and algorithm design

  • Excellent problem-solving skills with a creative, research-driven mindset

  • Demonstrated ability to work collaboratively in team-oriented environments

  • A passion for solving complex problems and a drive to innovate in a fast-paced, competitive environment

  • Visa sponsorship is available for this position


The annual base pay for this role is $300,000. Susquehanna considers factors such as scope and responsibilities of the position, work experience, education/training, key skills, as well as market and organizational considerations when extending an offer.


What we offer

  • Collaborate with a world-class team of researchers, engineers, and traders

  • Gain access to best-in-class financial data and high-performance computing resources

  • Directly impact real-time trading performance through your work

  • Thrive in a collaborative, intellectually rigorous environment with a global footprint


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.


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