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Physics Informed Machine Learning Jobs in New York

Mercor

Machine Learning Engineer

New York, NY · Remote

$70 - $100/hr

... experience in Machine Learning , Data Science , Software Engineering , Computer Science ... Civil Engineering , Physics , Chemistry , Mathematics , Materials Science , or other STEM ...

Siemens

Principal Applied Scientist

Manhattan, NY

The work sits at the intersection of machine learning research, real world data, and production ... Scientific ML for physical systems: surrogate modeling, operator learning, physics-informed ML ...

Siemens

Senior Applied Scientist

New York, NY · On-site

$167K - $226K/yr

The work sits at the intersection of machine learning research, real world data, and production ... Scientific ML for physical systems: surrogate modeling, operator learning, physics-informed ML ...

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 ...

Mercor

Senior Machine Learning Engineer

New York, NY · Remote

$70 - $100/hr

... experience in Machine Learning , Data Science , Software Engineering , Computer Science ... Civil Engineering , Physics , Chemistry , Mathematics , Materials Science , or other STEM ...

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:

Applied Physics

Data Scientist

New York, NY

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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Physics Informed Machine Learning information

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 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 is a Physics Informed Machine Learning job?

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 popular job titles related to Physics Informed Machine Learning jobs in New York? For Physics Informed Machine Learning jobs in New York, the most frequently searched job titles are:
What job categories do people searching Physics Informed Machine Learning jobs in New York look for? The top searched job categories for Physics Informed Machine Learning jobs in New York are:
What cities in New York are hiring for Physics Informed Machine Learning jobs? Cities in New York 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 New York as of July 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.
Machine Learning Engineer

Machine Learning Engineer

Mercor

New York, NY • Remote

$70 - $100/hr

Full-time

Re-posted 29 days ago

Job description

About the job

Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey.

Position: Expert Professionals — AI & Data Science
Type: Contract
Compensation: $70–$100/hour
Location: Remote
Commitment: 40 hours/week

Role Responsibilities

  • Guide research and engineering teams to close knowledge gaps in AI and data science domains. Surface nuances that distinguish expert-level work from surface-level reasoning.
  • Design challenging agentic tasks rooted in real-world ML, data science, data engineering, and software workflows. Write accurate, well-documented solutions that serve as ground truth.
  • Evaluate AI agent outputs against your solutions. Provide detailed written feedback capturing correctness, efficiency, and reasoning quality.
  • Develop and refine evaluation frameworks and rubrics for assessing agentic behavior on AI and data science tasks.
  • Collaborate with other subject matter experts to ensure consistency and accuracy in training data.

Qualifications

Must-Have

  • 3+ years of research, academic, or industry experience in Machine Learning, Data Science, Software Engineering, Computer Science, Statistics, Biology, Electrical/Mechanical/Civil Engineering, Physics, Chemistry, Mathematics, Materials Science, or other STEM background.
  • Demonstrated technical expertise in programming, data analysis, ML modeling, statistical methods, or computational methods.
  • Ability to commit to 40 hours per week during weekdays for the duration of the engagement.
  • Strong written communication skills and the ability to explain technical decisions clearly.

Preferred

  • Prior experience with data annotation, labeling, evaluation, or human feedback collection.
  • Experience with LLMs, AI systems, or agentic workflows; familiarity with agentic frameworks.

Application Process (Takes 20–30 mins to complete)

  • Upload resume
  • AI interview based on your resume
  • Submit form

Resources & Support

  • For details about the interview process and platform information, please check: https://talent.docs.mercor.com/welcome
  • For any help or support, reach out to: support@mercor.com

PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.