Field AI
1.3 Physics-Informed ML Engineer: Model Architectures
Field AI - Boston, Massachusetts, us, 02298
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Overview
1.3 Physics-Informed ML Engineer: Model Architectures
1.3 Physics-Informed ML Engineer: Model Architectures
1 week ago Be among the first 25 applicants Field AIis transforming how robots interact with the real world. We are building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robotics, unlocking the full potential of embodied intelligence. We go beyond typical data-driven approaches or pure transformer-based architectures, and are charting a new course, with already-globally-deployed solutions delivering real-world results and rapidly improving models through real-field applications. We are seeking a Physics-Informed Machine Learning (PIML) Engineer to join our innovative team focused on advancing risk-aware, autonomous systems. This role blends cutting-edge machine learning techniques with a strong foundation in physics, with an emphasis on safety, uncertainty quantification, and system robustness in real-world applications. The ideal candidate will work on integrating physical laws and constraints into machine learning models to create systems that learn from fewer data points while maintaining high accuracy and reliability in critical environments. Field AIis transforming how robots interact with the real world. We are building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robotics, unlocking the full potential of embodied intelligence. We go beyond typical data-driven approaches or pure transformer-based architectures, and are charting a new course, with already-globally-deployed solutions delivering real-world results and rapidly improving models through real-field applications. We are seeking a Physics-Informed Machine Learning (PIML) Engineer to join our innovative team focused on advancing risk-aware, autonomous systems. This role blends cutting-edge machine learning techniques with a strong foundation in physics, with an emphasis on safety, uncertainty quantification, and system robustness in real-world applications. The ideal candidate will work on integrating physical laws and constraints into machine learning models to create systems that learn from fewer data points while maintaining high accuracy and reliability in critical environments. What You Will Get To Do
Develop
hybrid physics-ML models
that combine theoretical
physics-based components with data-driven elements
to create more accurate and generalizable robotics autonomy solutions Design
physics-informed architectures
(e.g., physics-informed neural networks or universal differential equations) to solve complex robotic systems while respecting physical constraints like conservation of momentum, contact dynamics, and joint limits Lead research initiatives in
physics-informed learning for robot control , combining model-based and model-free approaches, solving
forward and inverse problems in robotic systems
using PIML Create
discrepancy models
to bridge theoretical physics models with empirical data, analyzing the convergence, generalization, and error estimation of PIML models, ensuring stability and robustness in deployment Design and evaluate
novel neural network architectures
that respect physical laws and constraints Build and optimize
differentiable simulation pipelines
for robot trajectory and control policy optimization, addressing complex physical constraints such as
uncertainty in perception systems Develop
uncertainty-aware models
combining physical knowledge with
probabilistic state estimation
(e.g., SDEs, Bayesian inference) for improved perception and intelligence Implement
multi-scale modeling
and
domain decomposition
to address large-scale challenges in autonomous robotics Collaborate with robotics teams to
deploy physics-informed models
in real-world autonomous systems Publish research in
physics-informed machine learning
and
hybrid modeling
for robotic systems
What You Have
Ph.D . or
M.S . in
Computer Science, Physics, Applied Mathematics , or related field with focus on
robot learning
and
physical systems Track record of combining
physics-informed machine learning
techniques, with practical experience applying them to
robotic systems Experience integrating
physical constraints
into
machine learning architectures Strong understanding of
POMDPs ,
differential equations ,
numerical methods , and
computational physics Proficiency in implementing both
physics-based
and
machine learning models Knowledge of conservation laws, symmetries, invariances, and conservation laws relevant to robotic systems (e.g.,
SE(3) equivariance, Lie groups, Noether’s theorem
to encode symmetries and invariances into
geometric deep learning models
for robotics) Experience with
differentiable programming frameworks (PyTorch, JAX) and robotics middleware Strong programming skills in
Python, C++, or Julia , with experience deploying algorithms on real
robots
Compensation and Benefits Our salary range is generous ($70,000 - $200,000 annual), but we take into consideration an individual's background and experience in determining final salary; base pay offered may vary considerably depending on geographic location, job-related knowledge, skills, and experience. Also, while we enjoy being together on-site, we are open to exploring a hybrid or remote option. Why Join Field AI? We are solving one of the world’s most complex challenges: deploying robots in unstructured, previously unknown environments. Our Field Foundational Models set a new standard in perception, planning, localization, and manipulation, ensuring our approach is explainable and safe for deployment. You will have the opportunity to work with a world-class team that thrives on creativity, resilience, and bold thinking. With a decade-long track record of deploying solutions in the field , winning DARPA challenge segments, and bringing expertise from organizations like DeepMind, NASA JPL, Boston Dynamics, NVIDIA, Amazon, Tesla Autopilot, Cruise Self-Driving, Zoox, Toyota Research Institute, and SpaceX, we are set to achieve our ambitious goals. Be Part of the Next Robotics Revolution To tackle such ambitious challenges, we need a team as unique as our vision — innovators who go beyond conventional methods and are eager to tackle tough, uncharted questions. We’re seeking individuals who challenge the status quo, dive into uncharted territory, and bring interdisciplinary expertise. Our team requires not only top AI talent but also exceptional software developers, engineers, product designers, field deployment experts, and communicators. We are headquartered in always-sunny Mission Viejo (Irvine adjacent), Southern California and have US based and global teammates. Join us, shape the future, and be part of a fun, close-knit team on an exciting journey! We celebrate diversity and are committed to creating an inclusive environment for all employees. Candidates and employees are always evaluated based on merit, qualifications, and performance. We will never discriminate on the basis of race, color, gender, national origin, ethnicity, veteran status, disability status, age, sexual orientation, gender identity, martial status, mental or physical disability, or any other legally protected status.
Seniority level
Seniority level Not Applicable Employment type
Employment type Full-time Job function
Job function Engineering and Information Technology Referrals increase your chances of interviewing at Field AI by 2x Get notified about new Machine Learning Engineer jobs in
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1.3 Physics-Informed ML Engineer: Model Architectures
1 week ago Be among the first 25 applicants Field AIis transforming how robots interact with the real world. We are building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robotics, unlocking the full potential of embodied intelligence. We go beyond typical data-driven approaches or pure transformer-based architectures, and are charting a new course, with already-globally-deployed solutions delivering real-world results and rapidly improving models through real-field applications. We are seeking a Physics-Informed Machine Learning (PIML) Engineer to join our innovative team focused on advancing risk-aware, autonomous systems. This role blends cutting-edge machine learning techniques with a strong foundation in physics, with an emphasis on safety, uncertainty quantification, and system robustness in real-world applications. The ideal candidate will work on integrating physical laws and constraints into machine learning models to create systems that learn from fewer data points while maintaining high accuracy and reliability in critical environments. Field AIis transforming how robots interact with the real world. We are building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robotics, unlocking the full potential of embodied intelligence. We go beyond typical data-driven approaches or pure transformer-based architectures, and are charting a new course, with already-globally-deployed solutions delivering real-world results and rapidly improving models through real-field applications. We are seeking a Physics-Informed Machine Learning (PIML) Engineer to join our innovative team focused on advancing risk-aware, autonomous systems. This role blends cutting-edge machine learning techniques with a strong foundation in physics, with an emphasis on safety, uncertainty quantification, and system robustness in real-world applications. The ideal candidate will work on integrating physical laws and constraints into machine learning models to create systems that learn from fewer data points while maintaining high accuracy and reliability in critical environments. What You Will Get To Do
Develop
hybrid physics-ML models
that combine theoretical
physics-based components with data-driven elements
to create more accurate and generalizable robotics autonomy solutions Design
physics-informed architectures
(e.g., physics-informed neural networks or universal differential equations) to solve complex robotic systems while respecting physical constraints like conservation of momentum, contact dynamics, and joint limits Lead research initiatives in
physics-informed learning for robot control , combining model-based and model-free approaches, solving
forward and inverse problems in robotic systems
using PIML Create
discrepancy models
to bridge theoretical physics models with empirical data, analyzing the convergence, generalization, and error estimation of PIML models, ensuring stability and robustness in deployment Design and evaluate
novel neural network architectures
that respect physical laws and constraints Build and optimize
differentiable simulation pipelines
for robot trajectory and control policy optimization, addressing complex physical constraints such as
uncertainty in perception systems Develop
uncertainty-aware models
combining physical knowledge with
probabilistic state estimation
(e.g., SDEs, Bayesian inference) for improved perception and intelligence Implement
multi-scale modeling
and
domain decomposition
to address large-scale challenges in autonomous robotics Collaborate with robotics teams to
deploy physics-informed models
in real-world autonomous systems Publish research in
physics-informed machine learning
and
hybrid modeling
for robotic systems
What You Have
Ph.D . or
M.S . in
Computer Science, Physics, Applied Mathematics , or related field with focus on
robot learning
and
physical systems Track record of combining
physics-informed machine learning
techniques, with practical experience applying them to
robotic systems Experience integrating
physical constraints
into
machine learning architectures Strong understanding of
POMDPs ,
differential equations ,
numerical methods , and
computational physics Proficiency in implementing both
physics-based
and
machine learning models Knowledge of conservation laws, symmetries, invariances, and conservation laws relevant to robotic systems (e.g.,
SE(3) equivariance, Lie groups, Noether’s theorem
to encode symmetries and invariances into
geometric deep learning models
for robotics) Experience with
differentiable programming frameworks (PyTorch, JAX) and robotics middleware Strong programming skills in
Python, C++, or Julia , with experience deploying algorithms on real
robots
Compensation and Benefits Our salary range is generous ($70,000 - $200,000 annual), but we take into consideration an individual's background and experience in determining final salary; base pay offered may vary considerably depending on geographic location, job-related knowledge, skills, and experience. Also, while we enjoy being together on-site, we are open to exploring a hybrid or remote option. Why Join Field AI? We are solving one of the world’s most complex challenges: deploying robots in unstructured, previously unknown environments. Our Field Foundational Models set a new standard in perception, planning, localization, and manipulation, ensuring our approach is explainable and safe for deployment. You will have the opportunity to work with a world-class team that thrives on creativity, resilience, and bold thinking. With a decade-long track record of deploying solutions in the field , winning DARPA challenge segments, and bringing expertise from organizations like DeepMind, NASA JPL, Boston Dynamics, NVIDIA, Amazon, Tesla Autopilot, Cruise Self-Driving, Zoox, Toyota Research Institute, and SpaceX, we are set to achieve our ambitious goals. Be Part of the Next Robotics Revolution To tackle such ambitious challenges, we need a team as unique as our vision — innovators who go beyond conventional methods and are eager to tackle tough, uncharted questions. We’re seeking individuals who challenge the status quo, dive into uncharted territory, and bring interdisciplinary expertise. Our team requires not only top AI talent but also exceptional software developers, engineers, product designers, field deployment experts, and communicators. We are headquartered in always-sunny Mission Viejo (Irvine adjacent), Southern California and have US based and global teammates. Join us, shape the future, and be part of a fun, close-knit team on an exciting journey! We celebrate diversity and are committed to creating an inclusive environment for all employees. Candidates and employees are always evaluated based on merit, qualifications, and performance. We will never discriminate on the basis of race, color, gender, national origin, ethnicity, veteran status, disability status, age, sexual orientation, gender identity, martial status, mental or physical disability, or any other legally protected status.
Seniority level
Seniority level Not Applicable Employment type
Employment type Full-time Job function
Job function Engineering and Information Technology Referrals increase your chances of interviewing at Field AI by 2x Get notified about new Machine Learning Engineer jobs in
Boston, MA . Boston, MA $125,000.00-$178,000.00 2 weeks ago Boston, MA $150,000.00-$220,000.00 1 month ago Boston, MA $94,000.00-$99,000.00 1 day ago Boston, MA $160,000.00-$280,000.00 1 year ago Machine Learning Engineer, Personalization
Boston, MA $136,878.00-$195,540.00 2 weeks ago Boston, MA $138,250.00-$197,500.00 2 weeks ago Boston, MA $130,000.00-$180,000.00 6 months ago Boston, MA $119,000.00-$169,000.00 1 month ago Research Scientist, Machine Learning / AI
Cambridge, MA $120,000.00-$180,000.00 6 months ago Cambridge, MA $120,000.00-$140,000.00 3 weeks ago Boston, MA $125,000.00-$178,000.00 4 months ago Quincy, MA $93,840.00-$140,760.00 1 day ago Cambridge, MA $160,000.00-$200,000.00 5 days ago Cambridge, MA $120,000.00-$140,000.00 2 weeks ago Cambridge, MA $120,000.00-$140,000.00 4 hours ago We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.
#J-18808-Ljbffr