Medium
1.3 Physics-Informed ML Engineer: Model Architectures
Medium, Boston, Massachusetts, us, 02298
Field AI is 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 between $70,000 and $300,000 annually, with final determination based on experience and location. We offer a hybrid or remote work arrangement where possible. 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. To tackle such ambitious challenges, we need a team of 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. Be Part of the Next Robotics Revolution
We celebrate diversity and are committed to creating an inclusive environment for all employees. Candidates and employees are 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, marital status, mental or physical disability, or any other legally protected status.
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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 between $70,000 and $300,000 annually, with final determination based on experience and location. We offer a hybrid or remote work arrangement where possible. 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. To tackle such ambitious challenges, we need a team of 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. Be Part of the Next Robotics Revolution
We celebrate diversity and are committed to creating an inclusive environment for all employees. Candidates and employees are 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, marital status, mental or physical disability, or any other legally protected status.
#J-18808-Ljbffr