FieldAI · 3 months ago
1.3 Physics-Informed ML Engineer: Model Architectures
Irvine, CA
Full-time
Hybrid
Senior Level
$70K/yr - $300K/yrFieldAI is transforming how robots interact with the real world by building advanced AI systems for robotics. The Physics-Informed Machine Learning Engineer will focus on integrating physical laws into machine learning models to enhance the reliability and accuracy of autonomous systems in complex environments.
Enterprise SoftwareRoboticsRobotic Process Automation (RPA)
Responsibilities
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
Qualification
Required
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
Company
FieldAI
FieldAI is the general-purpose brain making robots autonomous in complex, risky, real-world environments.
Founded in 2023
51-200 employees
H1B Sponsorship
FieldAI has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2026 (6)
2025 (9)
Funding
Current Stage
Growth StageTotal Funding
$405MKey Investors
Hyundai Motor GroupBezos Expeditions,Prysm Capital,Temasek Holdings
2026-02-22Corporate Round
2025-08-20Series Unknown· $91M
2025-08-20Series A· $314M
Leadership Team
Ali Agha
Founder and CEO
Recent News
Crunchbase News
2025-12-19
Company data provided by crunchbase