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Physics Informed Machine Learning Jobs in Allston, MA

MORSE Corp

Machine Learning Engineer

Cambridge, MA · On-site

We are seeking a Machine Learning Engineer to join our team at MORSE. You will play a pivotal role ... D. in Computer Science, Computer Engineering, Data Science, Aerospace, Mathematics, Physics, or ...

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

As of May 30, 2026, the average hourly pay for physics informed machine learning in Allston, MA is $21.89, according to ZipRecruiter salary data. Most workers in this role earn between $13.65 and $27.79 per hour, depending on experience, location, and employer.

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 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.
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AI/ML Engineer for Multidisciplinary Engineering Design-Associate Staff

AI/ML Engineer for Multidisciplinary Engineering Design-Associate Staff

MIT Lincoln Laboratory

Lexington, MA • On-site

Full-time

Posted 12 hours ago

Job description

Job Summary:
MIT Lincoln Laboratory is a leader in innovative engineering solutions for national security applications, and they are seeking an AI/ML Engineer for their Structural & Thermal-Fluids Engineering Group. The role involves developing AI/ML algorithms and engineering modeling expertise to solve complex engineering design problems across various applications, contributing to the development of operational prototype hardware.
Responsibilities:
• Support a broad range of simulation, design, optimization, and test activities.
• Develop innovative simulation capabilities to solve challenging engineering design problems across a broad array of applications ranging from low-speed aircraft to hypersonic systems and satellite design.
• Employ machine learning methods within the modeling suite to achieve significant improvements in physics-based simulation.
• Enhance existing methods and implement new cutting-edge AI/ML techniques to enable rapid and accurate concept design for complex and multidisciplinary problems.
• Contribute to the development of operational prototype hardware, spanning the full program life cycle from concept development to fielding and testing.
Qualifications:
Required:
• M.S. in Aerospace Engineering, Mechanical Engineering, Computer Science, or related. Candidates with B.S. degree with three years’ experience will also be considered.
• Experience developing and applying novel artificial intelligence and machine learning algorithms to solve engineering or scientific applications, such as through physics-informed machine learning, data-driven surrogate modeling, reinforcement learning, or agentic AI.
• Proficiency in C++, Python, MATLAB, or similar for algorithm development and software integration.
• Experience applying engineering software to design and analysis, e.g., fluid, structural, or thermal simulations.
• Ability to work within an interdisciplinary team.
• Ability to clearly communicate results in oral presentations and written reports.
Preferred:
• Experience with high-performance computing (HPC) environments for large-scale simulations
• Experience implementing multidisciplinary design optimization (MDO) frameworks.
• Familiarity with software development best practices, including version control (e.g., Git).
Company:
MIT Lincoln Laboratory is a federally funded research and development center chartered to apply advanced technology to problems of national security. Founded in 1951, the company is headquartered in Lexington, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

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