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Physics Informed Machine Learning Jobs in Bronx, NY

Root Access, Inc.

Machine Learning Engineer

Manhattan, NY · On-site

$150 - $200/hr

Architect Physics Foundation Models: Design and train deep learning models. * Build the ECAD Data ... Scientific Machine Learning (SciML). * Deep Learning Frameworks: 4+ years of expert-level ...

Root Access Inc

Machine Learning Engineer

New York, NY · On-site

Architect Physics Foundation Models: Design and train deep learning models. * Build the ECAD Data ... Scientific Machine Learning (SciML). * Deep Learning Frameworks: 4+ years of expert-level ...

PhysicsX

Machine Learning Engineer

New York, NY · On-site

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

CREW Carbon, Inc.

Full-Stack Data Scientist

Manhattan, NY · On-site

$100 - $125/hr

MRV, wastewater modeling, process-based modeling, geospatial data analysis & modeling, physics-informed machine learning. * Comfortable working in a fast-paced, early-stage, or evolving company ...

CREW

Full-Stack Data Scientist

Manhattan, NY · On-site

$100 - $125/hr

MRV, wastewater modeling, process-based modeling, geospatial data analysis & modeling, physics-informed machine learning. * Comfortable working in a fast-paced, early-stage, or evolving company ...

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

As of Sep 7, 2026, the average hourly pay for physics informed machine learning in Bronx, NY is $20.90, according to ZipRecruiter salary data. Most workers in this role earn between $13.03 and $26.54 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 are popular job titles related to Physics Informed Machine Learning jobs in Bronx, NY?

For Physics Informed Machine Learning jobs in Bronx, NY, the most frequently searched job titles are:

What job categories do people searching Physics Informed Machine Learning jobs in Bronx, NY look for?

The top searched job categories for Physics Informed Machine Learning jobs in Bronx, NY are:

What cities near Bronx, NY are hiring for Physics Informed Machine Learning jobs?

Cities near Bronx, NY 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 Bronx, NY 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 $43,474 per year, or $20.9 per hour.

Machine Learning Specialist

Applied Physics

New York, NY

Full-time

Re-posted 22 days ago

Job description

Applied Physics is seeking a highly motivated and skilled professional to join our Machine Learning team at the Advanced Propulsion Laboratory at Applied Physics. In this role, you will have the opportunity to work on cutting-edge research in new and emerging fields.

Responsibilities:

  • Conduct research on state-of-the-art Machine Learning algorithms relevant to the problem being addressed.
  • Implement, train, and validate proposed algorithms for specific problem domains.
  • Contribute to the integration of algorithms within larger programmatic systems that require these capabilities.
  • Collaborate with others in a multidisciplinary team environment to accomplish research goals.
  • Pursue both independent and collaborative research interests and interact with a broad spectrum of scientists internally and externally to the Laboratory.
  • Publish research results in peer-reviewed scientific journals and present results at conferences, seminars, and meetings.
  • Travel as required to coordinate research with collaborators and visit field sites.

Requirements

  • PhD in Computer Science, Computational Engineering, Applied Statistics, Applied Mathematics, or another technical discipline providing an underlying skillset in data analysis and Machine Learning techniques.
  • Fundamental knowledge of and/or experience developing and applying algorithms in one or more of the following Machine Learning areas/tasks: deep learning, representation learning, zero- or few-shot learning, active learning, reinforcement learning, natural language processing, ensemble methods, statistical modeling and inference (e.g., probabilistic graphical models, Gaussian processes, or nonparametric Bayesian methods).
  • Experience in the broad application of one or more higher-level programming languages such as Python, Java, Scala, or C/C++.
  • Experience with one or more deep learning libraries such as PyTorch, TensorFlow, Keras, or Caffe.
  • Proven ability to undertake original research and communicate findings in peer-reviewed publications.
  • Experience working with a multidisciplinary team of scientists, engineers, and project managers to develop and apply these capabilities to inform engineering decisions.
  • Proficient verbal and written communication skills to collaborate effectively in a team environment and present and explain technical information.

Benefits

We offer a competitive salary and benefits package, flexible work hours, and opportunities for growth and career development. Join our dynamic and passionate team and help us make a positive impact on the world.

If you are a talented, motivated, and empathetic individual who shares our passion for making a difference, we encourage you to apply for this exciting opportunity to work with our team at Applied Physics. Applied Physics is an equal opportunity employer.

Apply