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

As of Jun 20, 2026, the average hourly pay for physics informed machine learning in Boston, MA is $21.80, according to ZipRecruiter salary data. Most workers in this role earn between $13.56 and $27.69 per hour, depending on experience, location, and employer.

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 Boston, MA? For Physics Informed Machine Learning jobs in Boston, MA, the most frequently searched job titles are:
What job categories do people searching Physics Informed Machine Learning jobs in Boston, MA look for? The top searched job categories for Physics Informed Machine Learning jobs in Boston, MA are:
What cities near Boston, MA are hiring for Physics Informed Machine Learning jobs? Cities near Boston, MA 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 Boston, MA as of June 2026, with employment types broken down into 1% Locum Tenens, 79% Full Time, 15% Part Time, 1% Temporary, 2% Contract, and 2% Nights. Highlights an 72% Physical, 3% Hybrid, and 25% Remote job distribution, with an average salary of $45,336 per year, or $21.8 per hour.

Co-op, Machine Learning for Digital Twins

Lila Sciences

Cambridge, MA • On-site, Remote

Other

Posted 8 days ago

Job description

Your Impact at LILA

Lila Sciences builds AI systems that accelerate discovery across the physical and life sciences. Within Physical Sciences AI, our team partners with the diverse experimental groups to build digital twins of experimental campaigns, focusing on calibrated, uncertainty-aware models that enable higher-throughput, higher-quality use of Lila's AI Science Facilities (AISF).

As an ML for Digital Twins Co-Op, you will work on building, training, and evaluating ML models for physical and experimental systems. You will get hands-on experience with operator learning, surrogate modeling, and uncertainty quantification, shipping work that directly informs how next-generation AISF experiments are designed and run.

What You'll Be Building

  • Contribute to ML models for scientific and experimental systems, focused on a well-defined digital twin sub-problem
  • Build and train surrogate, operator-learning, or physics-informed models against experimental and simulation data, with mentor guidance
  • Calibrate models, quantify uncertainty, and validate against data flowing from active AISF experimental campaigns
  • Frame open-ended scientific questions as concrete ML tasks with clear datasets, baselines, and evaluation criteria
  • Document findings and share results in cross-departmental collaboration through write-ups and presentations

What You'll Need to Succeed

  • Pursuing a Master's or PhD in Machine Learning, Computer Science, Applied Mathematics, Physics, Materials Science, Chemical Engineering, Mechanical Engineering, Electrical Engineering, or a related quantitative field (PhD preferred)
  • Strong programming skills in Python and hands-on experience with ML frameworks such as PyTorch, JAX, TensorFlow, or similar
  • Experience applying machine learning to scientific, engineering, physical, or experimental systems
  • Familiarity with neural operators, operator learning, spatiotemporal modeling, field prediction, dynamical systems, scientific computing, surrogate modeling, or physics-informed ML
  • Ability to turn open-ended scientific questions into concrete ML tasks with clear datasets, assumptions, baselines, and evaluation criteria
  • Solid foundation in model training, validation, debugging, experiment tracking, and performance evaluation
  • Comfort working with messy, heterogeneous, or evolving scientific datasets
  • Clear communication and interest in collaborating across ML, software engineering, and physical science teams

Bonus Points For

  • Experience with modern operator-learning methods, including Fourier Neural Operators, DeepONets, graph neural operators, transformer-based neural operators, attention-based operators, physics-informed operators, or operator learning for spatiotemporal systems
  • Experience with digital twins, model update, calibration, and uncertainty-aware scientific modeling, including online/offline model updating, simulator calibration, discrepancy modeling, uncertainty quantification, out-of-distribution detection, or reliability estimation
  • Experience with closed-loop scientific decision-making or physical science applications, including active learning, Bayesian optimization, design of experiments, experimental decision-making, or applications in materials science, chemistry, energy systems, catalysis, batteries, electrochemistry, additive manufacturing, fluid dynamics, thermodynamics, robotics, or computational physics

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