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Member of Technical Staff, Applied AI

psiBostonTechnical Staff
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Salary
Not listed
Job type
Full-time
Work mode
Remote
Experience
Senior
Posted
7 Oct 2026
Status
Open · checked 7d ago

About this role

Physical Superintelligence is hiring a Member of Technical Staff in Boston for an Applied AI role that suits applied physicists, traditional engineers, and computational scientists who want to build AI systems for physical engineering.

Role Overview

Physical Superintelligence seeks technical staff to apply artificial intelligence to real-world engineering systems such as thermal and fluid networks, power distribution, and structures. The position involves building models and simulations that outperform traditional solvers and actively design new engineering solutions rather than simply evaluating existing ones.

Key Responsibilities

Build simulations, surrogates, and multiphysics models for thermal, fluid, electrical, and structural domains that surpass traditional workflows in speed and accuracy, and validate them against solvers and measured data.
Model domains at engineering depth, pairing classical studies with learned models and optimization for conjugate heat transfer, airflow, power distribution, structural analysis, and electrochemistry.
Train models using classical simulators that operate faster than replacement solvers and remain robust on inputs outside the training distribution.
Utilize models and agents to search design spaces and close the loop from analysis to design.
Structure simulation environments so that agents can interact with them effectively, determining where solver fidelity or learned models are appropriate.
Deliver working software solutions to customer problems and publish methods that advance the organizational mission.

Requirements

Hold a PhD or an equivalent research record in mechanical, electrical, aerospace, or chemical engineering, applied physics, computational science, or machine learning for science.
Demonstrate domain depth in at least one engineering area such as thermal and fluid systems, power systems, structural analysis, or electrochemistry, along with relevant domain tools.
Possess hands-on computational engineering experience with CFD, FEA, power system, or multiphysics codes, understanding discretization, convergence, and boundary conditions.
Apply machine learning independently using PyTorch or JAX, with practical experience designing training runs and deploying models for physics problems.
Experience deploying models into production environments behind APIs with proper testing, versioning, and reproducibility in HPC or cloud GPU settings.
Strong judgment to scope problems with domain engineers and deliver working artifacts.

Nice to Have

Familiarity with neural operators, physics-informed networks, differentiable simulation, or uncertainty quantification for learned models.
Experience utilizing open-source simulation software like OpenFOAM, OpenDSS, pandapower, or CalculiX.
Domain expertise in thermal and fluid systems, power systems, power electronics, structures, or electrochemistry.
Background in design space optimization including mixed-integer, derivative-free, or Bayesian search.
Peer-reviewed publications related to computational physics, engineering, or machine learning for science.

Skills and Tools

PyTorch
JAX
OpenFOAM
SU2
ETAP
PSS/E
PSCAD
OpenDSS
pandapower
CalculiX
Code_Aster
High-Performance Computing (HPC)
Cloud GPU environments

Salary and Benefits

Competitive compensation package
Salary and benefits
Meaningful early-stage equity

About the Company

Physical Superintelligence is a startup with roots at Google, NVIDIA, Harvard, Meta, MIT, Princeton, Oxford, Johns Hopkins, Cambridge, and the Perimeter Institute. The organization builds artificial intelligence systems designed to discover new physics at scale for broad public benefit. The company operates with an AI-native development process, utilizing agentic coding tools daily and emphasizing high technical ownership.

How to Apply

To apply for this position, please visit the official company careers page.

Skills and tech stack

PyTorchJAXOpenFOAMSU2ETAPPSS/EPSCADOpenDSSpandapowerCalculiXCode_AsterMachine LearningAirflow

Questions candidates ask

Where is this job located?

This role is based in Boston, though remote candidates may be considered on a case-by-case basis.

What background is required for this position?

Candidates must have a PhD or an equivalent research record in mechanical, electrical, aerospace, or chemical engineering, applied physics, computational science, or machine learning for science.

What machine learning frameworks are used in this role?

The position requires hands-on machine learning experience using PyTorch or JAX.

What compensation is provided?

The company offers competitive compensation including salary, benefits, and meaningful early-stage equity.

Is visa sponsorship available?

Visa sponsorship is not mentioned in the job posting.

Ready to apply?

The application is on the official psi careers page.

Apply on company site