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Physics Informed Machine Learning Jobs in Denver, CO

Product Development AI Engineer

Denver, CO ยท On-site

$115 - $125/hr

Exposure to physics-informed machine learning, digital twins, or hybrid physics/AI models * Experience deploying AI solutions in production or regulated engineering environments * Understanding of ...

New

Machine Learning Engineer

Aurora, CO ยท On-site

$120 - $180/hr

Machine Learning EngineerLOCATION Aurora, CO 80014 CLEARANCE TS/SCI Full Poly (Please note this ... Physics, ect. ALTERNATE EXPERIENCE General comment on degrees: Most contracts allow additional ...

Machine Learning Engineer LOCATION Aurora, CO 80014 CLEARANCE TS/SCI Full Poly (Please note this ... Physics, ect. ALTERNATE EXPERIENCE General comment on degrees: Most contracts allow additional ...

... drive data-informed decision-making across the enterprise. Oversee teams of data scientists ... Strategic Leadership and Vision Provide strategic direction for the organization's machine learning ...

New

Hydrologic Modeler

Boulder, CO ยท On-site +1

$81K - $112K/yr

Experience with machine-learning hydrologic modeling tools such as NeuralHydrology and dHBV, or comparable physics-informed and differentiable modeling approaches. * Experience developing or applying ...

Data Science Engineer

Westminster, CO ยท On-site

$100K - $140K/yr

... Physics, or a related quantitative field. 4+ years of applied machine learning experience (or ... Preferred Qualifications Experience with physics-informed ML or hybrid approaches that embed ...

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Physics Informed Machine Learning information

See Denver, CO salary details

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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 Denver, CO is $20.65, according to ZipRecruiter salary data. Most workers in this role earn between $12.88 and $26.25 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 Denver, CO?

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

What cities near Denver, CO are hiring for Physics Informed Machine Learning jobs?

Cities near Denver, CO with the most Physics Informed Machine Learning job openings:

Infographic showing various Physics Informed Machine Learning job openings in Denver, CO 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 $42,953 per year, or $20.7 per hour.

Product Development AI Engineer

Tribonet

Denver, CO โ€ข On-site

$115 - $125/hr

Other

Medical, Dental, Vision, Retirement, PTO

Posted 2 days ago

New


Job description

An educational platform on tribology โ€” the science of friction, wear and lubrication

Who is it for

Engineers, researchers, students and industry professionals

Are you inspired by challenging the status quo? Do you thrive in collaborative environments that drive results? If so, Gates could be for you.

Gates is a leading manufacturer of application-specific fluid power and power transmission solutions. We push the boundaries of material science to engineer solutions that continually exceed customer expectations.

Letโ€™s simplify it, think belts and hoses. Found in motorcycles, conveyor belts, cars, tractors, blenders, vacuum cleaners, bicycles, & 3D printers just to name a few. Because why not do it all?

Position Summary

Gates Corporation is seeking a Product Development AI Engineer to accelerate innovation across our global product portfolio by applying artificial intelligence, machine learning, and advanced analytics to engineering and product development processes.

This role will work at the intersection of engineering, data, and digital technology, partnering with product development teams and GPLM to improve design efficiency, predictive performance modeling, materials optimization, testing automation, and lifecycle management for Gatesโ€™ power transmission and fluid power products.

The ideal candidate combines strong AI/ML expertise with an understanding of engineering systems, physical products, and industrial workflows, enabling Gates to move faster from concept to commercialization while improving product performance, reliability, and quality.

Essential Duties And ResponsibilitiesAI-Driven Product Development
  • Design, develop, and deploy AI/ML models to support product design, simulation, testing, and validation activities.
  • Apply machine learning techniques to predict product performance, durability, and failure modes using historical test, field, and simulation data.
  • Develop AI-enabled tools for design optimization, including automated parameter tuning, materials selection, and geometry optimization.
Engineering & Domain Integration
  • Collaborate closely with fluid power and power transmission team, mechanical, materials team, and manufacturing engineers to embed AI solutions into existing product development workflows.
  • Integrate AI models with engineering tools such as CAD/CAE, FEA, CFD, PLM, and test lab systems.
  • Translate complex physical engineering problems into data-driven and AI-compatible formulations.
Advanced Analytics & Data Engineering
  • Curate, clean, and structure large datasets from test labs, manufacturing systems, field data, and supplier data.
  • Develop data pipelines and feature engineering approaches suitable for industrial-scale ML applications.
  • Ensure models are explainable, validated, and usable by nonโ€‘dataโ€‘science engineering teams.
Digital Transformation & Innovation
  • Contribute to Gatesโ€™ digital product development roadmap, identifying opportunities where AI can deliver measurable business value.
  • Prototype and industrialize AI solutions that reduce development cycle time, improve first-pass yield, and lower cost of poor quality.
  • Partner with IT, Digital, and Cybersecurity teams to ensure scalable, secure deployment.
Governance & Best Practices
  • Apply best practices for model lifecycle management, versioning, validation, and documentation.
  • Support responsible AI use, including transparency, robustness, and compliance with Gatesโ€™ engineering and quality standards.
  • Mentor engineers and developers on AI concepts and tools within the product development organization.
Requirements And Preferred Skills
  • Bachelors in Math, Computer Science, Data Science, Mechanical Engineering, Electrical Engineering, Materials Science, or a related field. Degrees at the Master or PhD level are preferred.Experience
  • 5-7+ years of experience applying machine learning or advanced analytics in an engineering, manufacturing, or industrial context
  • Demonstrated experience supporting physical product development (as opposed to pure digital products)
  • Hands-on experience working with engineering datasets (test data, sensor data, simulation results)
Technical Skills
  • Strong proficiency in Python and common ML libraries (e.g., TensorFlow, PyTorch, scikit-learn)
  • Experience with data analysis, feature engineering, and model evaluation
  • Familiarity with engineering tools and environments (e.g., CAD/CAE, PLM systems, test automation, industrial databases)
  • Working knowledge of statistics, optimization techniques, and numerical methods
Preferred Qualifications
  • Experience in industrial equipment, or advanced manufacturing
  • Exposure to physics-informed machine learning, digital twins, or hybrid physics/AI models
  • Experience deploying AI solutions in production or regulated engineering environments
  • Understanding of materials behavior, tribology, fatigue, thermal systems, or fluid dynamics
  • Experience working in Agile or hybrid product development environments
Key Competencies
  • Strong problem-solving skills with a system-level mindset
  • Ability to translate between engineering domain experts and data/AI practitioners
  • Clear technical communication and documentation skills
  • Curiosity, pragmatism, and a results-driven approach to innovation
Pay & Benefits
    • Full-Time
    • Salary: $115,000-$125,000
    • Medical, Dental, Vision insurance and other voluntary benefit options: benefits begin on the first day of the month immediately following your date of hire
    • Eligible for 3 weeks of paid vacation + 11 holidays (9 scheduled & 2 floating) + 8 sick days. All vacation days are accrued
    • 401(k): 3% company contribution and additional 3% company match
    • Tuition Reimbursement
WHY GATES?

Founded in 1911 in Denver, Colorado, Gates is publicly traded on the NYSE. While we might operate in a vast amount of time zones we operate as โ€˜One Gatesโ€™ and have a common goal of pushing the boundaries of materials science. We invest in our people, bringing real-world experience that enables us to solve our customersโ€™ diverse challenges of today and anticipate those of tomorrow.

WORK ENVIRONMENT

Gates is an Equal Opportunity and is committed to ensuring equal employment opportunities for all job applicants and employees. Employment decisions are based upon job-related reasons regardless of race, sex, color, religion, age, disability, pregnancy, citizenship, sexual orientation, gender identity, national origin, protected veteran status, genetic information, marital status, or any other consideration defined by law.

While performing the duties of this job, the employee is frequently required to sit; use hands and fingers to work with objects, tools, or controls; and use office equipment including computers, telephones, and/or copiers/scanners. The employee must frequently lift and/or move up to 10 pounds.

For individuals assigned and/or hired to work in Colorado, Gates is required by law to include a reasonable estimate of the compensation for this role. This compensation range is specific to the State of Colorado and takes into account various factors that are considered in making compensation decisions, including but not limited to the candidateโ€™s relevant experience, qualifications, skills, competencies, and proficiency for the role.

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