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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 Colorado Springs, CO is $19.77, according to ZipRecruiter salary data. Most workers in this role earn between $12.31 and $25.10 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 Colorado Springs, CO?

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

What job categories do people searching Physics Informed Machine Learning jobs in Colorado Springs, CO look for?

The top searched job categories for Physics Informed Machine Learning jobs in Colorado Springs, CO are:

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

Cities near Colorado Springs, CO 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 Colorado Springs, CO as of August 2026, with employment types broken down into 6% Internship, 42% Full Time, 45% Part Time, and 7% Contract. Highlights an 100% In-person job distribution, with an average salary of $41,125 per year, or $19.8 per hour.

Position, Navigation, and Timing (PNT) / Navigation Warfare (NAVWAR) Analyst - Clearance Required

LMI

Colorado Springs, CO • On-site

Part-time

Posted 10 days ago

Job description

OverviewWe are seeking a highly skilled PNT / NAVWAR Analyst to support the Space Warfighting Analysis Center (SWAC) in developing future force designs for Position, Navigation, and Timing capabilities. This is a high-impact analytical leadership role at the intersection of space systems, national security, and long-range investment planning. The selected candidate will lead analysis that shapes how the United States postures its PNT architecture more than a decade into the future, directly informing investment decisions across space-related government and civilian agencies. This role requires balancing the needs of the Space Force as a service provider with the needs of the end user, ensuring that proposed architectures are both technically sound and relevant across the full range of PNT and NAVWAR mission needs. The candidate will take a limited amount of customer direction, define the underlying problem, and build the analytical plan and team needed to solve it.  This is an on-site role in Colorado Springs, CO.

LMI is a new breed of digital solutions provider dedicated to accelerating government impact with innovation and speed. Investing in technology and prototypes ahead of need, LMI brings commercial-grade platforms and mission-ready AI to federal agencies at commercial speed.

Leveraging our mission-ready technology and solutions, proven expertise in federal deployment, and strategic relationships, we enhance outcomes for the government, efficiently and effectively. With a focus on agility and collaboration, LMI serves the defense, space, healthcare, and energy sectors-helping agencies navigate complexity and outpace change. Headquartered in Tysons, Virginia, LMI is committed to delivering impactful results that strengthen missions and drive lasting value.

Responsibilities
  • Design and implement physics-based models for space infrastructure, incorporating orbital dynamics, electromagnetic propagation, sensor phenomenology, environmental effects, and multi-domain interactions.
  • Conduct high-fidelity simulations and analyses of space systems, evaluating performance under various scenarios (e.g., nominal operations, contested environments, debris fields).
  • Develop analytical frameworks to assess system vulnerabilities, trade-offs, and optimization opportunities, using tools like Monte Carlo methods, sensitivity analysis, and uncertainty quantification.
  • Translate simulation results into actionable recommendations for force design, including architecture enhancements, resource allocation, and capability gaps.
  • Collaborate with software engineers, data scientists, and decision-makers to integrate models into broader analytical pipelines and decision-support systems.
  • Build and maintain scalable simulation environments, ensuring models are computationally efficient and validated against empirical data.
  • Perform data analysis on large datasets from simulations, telemetry, or open-source intelligence to derive insights and visualize outcomes.
  • Contribute to technical reports, briefings, and strategic recommendations for stakeholders, including potential publication or presentation at industry conferences.
  • Mentor junior team members and help shape best practices in space-domain physics modeling.
Qualifications

Required:

  • B.S. in Physics, Applied Physics, Aerospace Engineering, Operations Research, or a related field with a strong emphasis on theoretical modeling.
  • At least 4+ years of experience in space-related modeling, simulation, and analysis (industry, national lab, government, or advanced research settings).
  • Strong expertise in physics and theory relevant to space: orbital mechanics, astrodynamics, electromagnetics, radiation effects, satellite communications (ground and space-based), position, navigation, and timing (PNT) systems, and systems engineering principles.
  • Demonstrated ability to abstract theoretical concepts into practical models, simulations, and analytical frameworks that drive decision-making.
  • Experience with simulation tools such as STK (Systems Tool Kit), GMAT, FreeFlyer, AFSIM, or custom-built propagators.
  • Familiarity with force design concepts, mission analysis, or wargaming in space or multi-domain operations.
  • Strong problem-solving skills and the ability work independently with limited, if any, technical definition of analytical problems.
  • Excellent communication and teamwork abilities. 

Desired:

  • Advanced degree (M.S. or Ph.D.) in Physics, Applied Physics, Aerospace Engineering, Operations Research, or a related field with a strong emphasis on theoretical modeling.
  • Background in space domain infrastructure analysis, including constellation design, coverage modeling, or resilience assessments.
  • Experience with advanced techniques like physics-informed machine learning, agent-based modeling, or game theory for strategic recommendations.
  • Knowledge of high-performance computing (HPC), parallel processing, or cloud-based simulation environments (e.g., AWS, Azure). 

Security Clearance Requirements: Candidate must have a TS clearance and be eligible for an SCI.

Target salary range: $114,108 - $220,000

Disclaimer:  

The salary range displayed represents the typical salary range for this position and is not a guarantee of compensation. Individual salaries are determined by various factors including, but not limited to location, internal equity, business considerations, client contract requirements, and candidate qualifications, such as education, experience, skills, and security clearances.

Applicants must meet eligibility requirements for a U.S. Government security clearance. Only US Citizens are eligible for a security clearance. For this position, LMI will only consider applicants with security clearances or applicants who are eligible for security clearances, due to the nature of the work.

Job LocationsUS-CO-Colorado SpringsEmployment Type: PART_TIME

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