... Physics-Informed Neural Networks (PINNs), Optimization, Reinforcement Learning, classical Machine Learning, statistical modeling, and sensing-hardware prototyping. Rooted in first principles aligned ...
... Physics-Informed Neural Networks (PINNs), Optimization, Reinforcement Learning, classical Machine Learning, statistical modeling, and sensing-hardware prototyping. Rooted in first principles aligned ...
... Physics-Informed Neural Networks (PINNs), Optimization, Reinforcement Learning, classical Machine Learning, statistical modeling, and sensing-hardware prototyping. Rooted in first principles aligned ...
... Physics-Informed Neural Networks (PINNs), Optimization, Reinforcement Learning, classical Machine Learning, statistical modeling, and sensing-hardware prototyping. Rooted in first principles aligned ...
Familiarity with neural operators, operator learning, spatiotemporal modeling, field prediction, dynamical systems, scientific computing, surrogate modeling, or physics-informed ML * Ability to turn ...
Familiarity with neural operators, operator learning, spatiotemporal modeling, field prediction, dynamical systems, scientific computing, surrogate modeling, or physics-informed ML * Ability to turn ...
Quantum Calibrations Intern, Quantum Computing Services
Boston, MA · On-site
$16.25 - $21.75/hr
... physics-informed models of device behavior, and develop predictive tools for real-time qubit state ... Experience applying machine learning (Gaussian processes, time-series models, or neural networks ...
Quantum Calibrations Intern, Quantum Computing Services
Boston, MA · On-site
$16.25 - $21.75/hr
... physics-informed models of device behavior, and develop predictive tools for real-time qubit state ... Experience applying machine learning (Gaussian processes, time-series models, or neural networks ...
... Physics-Informed Neural Networks (PINNs), Optimization, Reinforcement Learning, classical Machine Learning, statistical modeling, and sensing-hardware prototyping. Rooted in first principles aligned ...
... Physics-Informed Neural Networks (PINNs), Optimization, Reinforcement Learning, classical Machine Learning, statistical modeling, and sensing-hardware prototyping. Rooted in first principles aligned ...
Postdoctoral Fellow in Deep Learning Theory and/or Theoretical Neuroscience
Cambridge, MA · On-site
$67K - $91K/yr
... in neural networks, which drive both artificial and natural intelligence. Current projects span a ... We seek candidates with strong analytical and numerical skills, and backgrounds in physics ...
Postdoctoral Fellow in Deep Learning Theory and/or Theoretical Neuroscience
Cambridge, MA · On-site
$67K - $91K/yr
... in neural networks, which drive both artificial and natural intelligence. Current projects span a ... We seek candidates with strong analytical and numerical skills, and backgrounds in physics ...
Senior Research Scientist - Machine Leaning
Woburn, MA · On-site
$139K - $165K/yr
... math, physics, electrical engineering, computer science, or data science * Experience building and training neural networks using standard deep learning tools (e.g., PyTorch, JAX, TensorFlow)
Senior Research Scientist - Machine Leaning
Woburn, MA · On-site
$139K - $165K/yr
... math, physics, electrical engineering, computer science, or data science * Experience building and training neural networks using standard deep learning tools (e.g., PyTorch, JAX, TensorFlow)
Senior Research Scientist - Machine Leaning
Woburn, MA · On-site
$105K - $134K/yr
... applied math, physics, electrical engineering, computer science, or data science • Experience building and training neural networks using standard deep learning tools (e.g., PyTorch, JAX ...
Senior Research Scientist - Machine Leaning
Woburn, MA · On-site
$105K - $134K/yr
... applied math, physics, electrical engineering, computer science, or data science • Experience building and training neural networks using standard deep learning tools (e.g., PyTorch, JAX ...
... math, physics, electrical engineering, computer science, or data science • Experience building neural networks using standard deep learning tools (e.g., PyTorch, JAX, TensorFlow), including ...
... math, physics, electrical engineering, computer science, or data science • Experience building neural networks using standard deep learning tools (e.g., PyTorch, JAX, TensorFlow), including ...
... math, physics, electrical engineering, computer science, or data science • Experience building neural networks using standard deep learning tools (e.g., PyTorch, JAX, TensorFlow), including ...
... math, physics, electrical engineering, computer science, or data science • Experience building neural networks using standard deep learning tools (e.g., PyTorch, JAX, TensorFlow), including ...
... math, physics, electrical engineering, computer science, or data science • Experience building neural networks using standard deep learning tools (e.g., PyTorch, JAX, TensorFlow), including ...
... math, physics, electrical engineering, computer science, or data science • Experience building neural networks using standard deep learning tools (e.g., PyTorch, JAX, TensorFlow), including ...
... math, physics, electrical engineering, computer science, or data science • Experience building neural networks using standard deep learning tools (e.g., PyTorch, JAX, TensorFlow), including ...
... math, physics, electrical engineering, computer science, or data science • Experience building neural networks using standard deep learning tools (e.g., PyTorch, JAX, TensorFlow), including ...
... math, physics, electrical engineering, computer science, or data science • Experience building neural networks using standard deep learning tools (e.g., PyTorch, JAX, TensorFlow), including ...
... math, physics, electrical engineering, computer science, or data science • Experience building neural networks using standard deep learning tools (e.g., PyTorch, JAX, TensorFlow), including ...
... math, physics, electrical engineering, computer science, or data science • Experience building neural networks using standard deep learning tools (e.g., PyTorch, JAX, TensorFlow), including ...
... math, physics, electrical engineering, computer science, or data science • Experience building neural networks using standard deep learning tools (e.g., PyTorch, JAX, TensorFlow), including ...
... math, physics, electrical engineering, computer science, or data science • Experience building neural networks using standard deep learning tools (e.g., PyTorch, JAX, TensorFlow), including ...
... math, physics, electrical engineering, computer science, or data science • Experience building neural networks using standard deep learning tools (e.g., PyTorch, JAX, TensorFlow), including ...
Senior Machine Learning Engineer - ML Planner
Boston, MA · On-site +1
$146K - $225K/yr
Experience designing, training, and analyzing neural networks for at least one of the following ... Statistics, Physics or a related field; or equivalent industry experience Bonus Points
Senior Machine Learning Engineer - ML Planner
Boston, MA · On-site +1
$146K - $225K/yr
Experience designing, training, and analyzing neural networks for at least one of the following ... Statistics, Physics or a related field; or equivalent industry experience Bonus Points
Senior Machine Learning Engineer - Prediction
Boston, MA · On-site +1
$146K - $225K/yr
Experience designing, training, and analyzing neural networks for at least one of the following ... Statistics, Physics or a related field; or equivalent industry experience Bonus Points
Senior Machine Learning Engineer - Prediction
Boston, MA · On-site +1
$146K - $225K/yr
Experience designing, training, and analyzing neural networks for at least one of the following ... Statistics, Physics or a related field; or equivalent industry experience Bonus Points
... math, physics, electrical engineering, computer science, or data science • Experience building neural networks using standard deep learning tools (e.g., PyTorch, JAX, TensorFlow), including ...
... math, physics, electrical engineering, computer science, or data science • Experience building neural networks using standard deep learning tools (e.g., PyTorch, JAX, TensorFlow), including ...
Senior Machine Learning Engineer - Prediction
Boston, MA · On-site +1
$146K - $225K/yr
Experience designing, training, and analyzing neural networks for at least one of the following ... Statistics, Physics or a related field; or equivalent industry experience Bonus Points
Quick apply
Senior Machine Learning Engineer - Prediction
Boston, MA · On-site +1
$146K - $225K/yr
Experience designing, training, and analyzing neural networks for at least one of the following ... Statistics, Physics or a related field; or equivalent industry experience Bonus Points
Lead Research Scientist - Machine Learning (Clearance Required)
Woburn, MA · On-site
$174K - $220K/yr
... math, physics, electrical engineering, computer science, or data science * Experience building neural networks using standard deep learning tools (e.g., PyTorch, JAX, TensorFlow), including ...
Lead Research Scientist - Machine Learning (Clearance Required)
Woburn, MA · On-site
$174K - $220K/yr
... math, physics, electrical engineering, computer science, or data science * Experience building neural networks using standard deep learning tools (e.g., PyTorch, JAX, TensorFlow), including ...
Physics Informed Neural Networks information
See Boston, MA salary details
$5.75 - $7.74
0% of jobs
$7.74 - $9.73
0% of jobs
$9.73 - $11.73
0% of jobs
$11.73 - $13.72
24% of jobs
$13.82 is the 25th percentile. Wages below this are outliers.
$13.72 - $15.72
16% of jobs
$15.72 - $17.71
0% of jobs
$17.71 - $19.71
0% of jobs
$19.71 - $21.70
0% of jobs
$21.70 - $23.69
0% of jobs
The median wage is $24.17 / hr.
$23.69 - $25.69
40% of jobs
$25.69 - $27.68
19% of jobs
$5
$21
$27
How much do physics informed neural networks jobs pay per hour?
As of Aug 8, 2026, the average hourly pay for physics informed neural networks 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 is a physics informed neural network?
A Physics Informed Neural Networks (PINNs) job typically involves developing and applying neural networks that incorporate physical laws as constraints to solve complex scientific and engineering problems. Professionals in this field work on integrating differential equations into deep learning models to improve predictions and reduce the need for large training datasets. These roles are common in fields like fluid dynamics, material science, and climate modeling, where traditional computational methods can be expensive. Individuals in this role often have expertise in machine learning, numerical methods, and domain-specific physics.
What are the key skills and qualifications needed to thrive in physics informed neural networks?
To thrive in Physics Informed Neural Networks (PINNs), you need a strong background in physics, mathematics, and deep learning frameworks, typically evidenced by advanced degrees in physics, applied mathematics, computer science, or engineering. Experience with programming languages such as Python, and familiarity with libraries like TensorFlow or PyTorch, as well as experience in numerical simulation tools, are commonly required. Strong analytical thinking, problem-solving abilities, and effective communication skills help professionals excel in multidisciplinary teams. These qualifications and soft skills are essential for developing accurate, interpretable models that integrate scientific knowledge with machine learning to solve complex real-world problems.
What does a physics informed neural network do?
In a Physics Informed Neural Networks role, your daily tasks will often include designing, building, and testing neural network architectures that incorporate physical laws and constraints. You will frequently collaborate with domain experts, such as physicists or engineers, to integrate scientific knowledge into machine learning models and validate the results with real-world data. Regular responsibilities also involve coding, running experiments, analyzing results, and documenting findings for presentation or publication. This collaborative and research-driven environment helps ensure that models are both accurate and physically consistent, and offers opportunities for interdisciplinary learning and skill advancement.
What are popular job titles related to Physics Informed Neural Networks jobs in Boston, MA? For Physics Informed Neural Networks jobs in Boston, MA, the most frequently searched job titles are:
What job categories do people searching Physics Informed Neural Networks jobs in Boston, MA look for? The top searched job categories for Physics Informed Neural Networks jobs in Boston, MA are:
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Full-time
Medical, Dental, Vision, Life, Retirement, PTO
Posted 4 days ago
Amazon rating
7.4
Based on 7,066 frontline employees who took The Breakroom Quiz
6th of 39 rated national retailers
Job description
As an Applied Scientist on the Science SW team, you will be a versatile generalist who collaborates closely with other scientists and engineers teams to bring
research to production across a broad portfolio of problems: from computer-vision perception platforms to building-wide optimization and orchestration. This role combines the scientific application of ML and applied mathematics with a strong product focus. It will be your job to frame ambiguous business problems as tractable scientific problems, and to implement novel ML systems, first-principles models, embedded systems prototypes, and performance optimizations in both prototype and production environments.
Key job responsibilities
• Own the research and development of scientific and ML solutions across a broad range of problems spanning classical machine learning, statistical modeling, computer vision, optimization, and physics-informed / first-principles modeling in a production environment.
• Rapidly ramp on unfamiliar problem domains, frame ambiguous or open-ended business problems as tractable scientific problems, and prototype solutions end to end.
• Prototype and evaluate sensing hardware and lightweight, edge-deployable models that run on commodity compute under real-world constraints.
• Collaborate across multiple science and engineering teams to integrate your solutions into our deployment architecture.
About the team
Amazon is building next generation software, hardware, and processes that will run our global network of fulfillment centers that move millions of units of inventory, and ensure customers get what they want when promised.
The Science Software team in the One MHS organization unlocks Material Handling Equipment (MHE) innovation through a multiplicity of disciplines within Artificial Intelligence (AI) and applied science, including Computer Vision (CV), Physics-Informed Neural Networks (PINNs), Optimization, Reinforcement Learning, classical Machine Learning, statistical modeling, and sensing-hardware
prototyping. Rooted in first principles aligned experimentation, the team is dedicated to building self-optimizing fulfillment centers, developing the models that drive real-time, building-wide orchestration of MHE. We conduct experiments,
develop models, and apply machine learning (ML) at scale to optimize throughput, flow, merge, and congestion control, and to improve operational performance across the fulfillment network.
BASIC QUALIFICATIONS
- Currently has, or is in the process of obtaining, a Advanced degree in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
- Experience with programming languages such as Python, Java, C++
- Strong foundation in applied mathematics, statistics, and machine learning, with the versatility to work across multiple problem domains rather than a single specialization.
- Experience with popular deep learning frameworks (e.g., PyTorch, TensorFlow) and the scientific Python stack (e.g., NumPy, SciPy, scikit-learn, pandas).
PREFERRED QUALIFICATIONS
- PhD with a demonstrated track record of solving problems across more than one domain (e.g., computer vision, statistical modeling, optimization, signal processing, controls, or physical modeling).
- Experience with computer vision and/or physics-informed and first-principles modeling.
- Hands-on hardware prototyping experience (sensors, cameras, embedded / edge compute) and experience optimizing models for resource-constrained hardware.
- Publications at peer-reviewed venues (e.g., CVPR, NeurIPS, ICML, ICLR, or the leading venues in the candidate's home discipline).
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you're applying in isn't listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, MA, Boston - 136,000.00 - 184,000.00 USD annually
USA, MA, N.Reading - 136,000.00 - 184,000.00 USD annually
USA, MA, Westboro - 136,000.00 - 184,000.00 USD annually
research to production across a broad portfolio of problems: from computer-vision perception platforms to building-wide optimization and orchestration. This role combines the scientific application of ML and applied mathematics with a strong product focus. It will be your job to frame ambiguous business problems as tractable scientific problems, and to implement novel ML systems, first-principles models, embedded systems prototypes, and performance optimizations in both prototype and production environments.
Key job responsibilities
• Own the research and development of scientific and ML solutions across a broad range of problems spanning classical machine learning, statistical modeling, computer vision, optimization, and physics-informed / first-principles modeling in a production environment.
• Rapidly ramp on unfamiliar problem domains, frame ambiguous or open-ended business problems as tractable scientific problems, and prototype solutions end to end.
• Prototype and evaluate sensing hardware and lightweight, edge-deployable models that run on commodity compute under real-world constraints.
• Collaborate across multiple science and engineering teams to integrate your solutions into our deployment architecture.
About the team
Amazon is building next generation software, hardware, and processes that will run our global network of fulfillment centers that move millions of units of inventory, and ensure customers get what they want when promised.
The Science Software team in the One MHS organization unlocks Material Handling Equipment (MHE) innovation through a multiplicity of disciplines within Artificial Intelligence (AI) and applied science, including Computer Vision (CV), Physics-Informed Neural Networks (PINNs), Optimization, Reinforcement Learning, classical Machine Learning, statistical modeling, and sensing-hardware
prototyping. Rooted in first principles aligned experimentation, the team is dedicated to building self-optimizing fulfillment centers, developing the models that drive real-time, building-wide orchestration of MHE. We conduct experiments,
develop models, and apply machine learning (ML) at scale to optimize throughput, flow, merge, and congestion control, and to improve operational performance across the fulfillment network.
BASIC QUALIFICATIONS
- Currently has, or is in the process of obtaining, a Advanced degree in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
- Experience with programming languages such as Python, Java, C++
- Strong foundation in applied mathematics, statistics, and machine learning, with the versatility to work across multiple problem domains rather than a single specialization.
- Experience with popular deep learning frameworks (e.g., PyTorch, TensorFlow) and the scientific Python stack (e.g., NumPy, SciPy, scikit-learn, pandas).
PREFERRED QUALIFICATIONS
- PhD with a demonstrated track record of solving problems across more than one domain (e.g., computer vision, statistical modeling, optimization, signal processing, controls, or physical modeling).
- Experience with computer vision and/or physics-informed and first-principles modeling.
- Hands-on hardware prototyping experience (sensors, cameras, embedded / edge compute) and experience optimizing models for resource-constrained hardware.
- Publications at peer-reviewed venues (e.g., CVPR, NeurIPS, ICML, ICLR, or the leading venues in the candidate's home discipline).
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you're applying in isn't listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, MA, Boston - 136,000.00 - 184,000.00 USD annually
USA, MA, N.Reading - 136,000.00 - 184,000.00 USD annually
USA, MA, Westboro - 136,000.00 - 184,000.00 USD annually
About Amazon
Sourced by ZipRecruiter
Amazon.com, Inc., commonly known as Amazon, is an American multinational technology company. It was founded by Jeff Bezos in 1994 and initially started as an online marketplace for books. Since then, Amazon has expanded its operations and become one of the largest e-commerce companies in the world. Amazon's primary business is its online retail platform, where customers can purchase a vast array of products, including electronics, clothing, books, home goods, and much more. The company offers a convenient and user-friendly shopping experience, with features such as fast shipping, customer reviews, and personalized recommendations. In addition to its e-commerce platform, Amazon has diversified its business into various other areas. One of its notable ventures is Amazon Web Services (AWS), a comprehensive cloud computing platform that provides services such as storage, compute power, and database management to individuals and businesses. AWS has become a leader in the cloud computing industry, powering many websites and applications worldwide. Amazon has also developed its own consumer electronics, including the popular Amazon Kindle e-reader, Fire tablets, Fire TV streaming devices, and the Alexa-powered Echo smart speakers. The Alexa voice assistant, integrated into these devices, allows users to interact with their devices using voice commands, perform tasks, and access information. Furthermore, Amazon has expanded into media and entertainment. It operates Prime Video, a streaming service that offers a wide range of movies, TV shows, and original content. Amazon Music provides a platform for streaming and purchasing digital music, while Audible offers audiobooks and other audio content. The company's commitment to customer satisfaction and convenience is demonstrated by its membership program, Amazon Prime. Prime members receive various benefits, including free two-day shipping, access to streaming services, exclusive deals, and more.
Industry
It services, book publishers, retail, real estate and computer and electronic product manufacturing
Company size
10,000+ Employees
Headquarters location
Seattle, WA, US