Applied AI Engineer - Physics-Based Modeling & ML

Acceler8 Talent; Massachusetts; Hybrid; USD 120,000 - 160,000; Full time

Job summary

Acceler8 Talent is partnering with an early-stage AI company to hire experienced Applied AI Engineers and Researchers in Cambridge, MA. You will develop physics-based models for complex engineering systems and blend numerical solvers with ML to boost simulation speed and fidelity.

You’ll build and validate scientific ML models against real measurements, apply neural operators and surrogate modelling, and translate research into deployable tools used by engineering teams in a hybrid setting (3

Qualifications

• Strong background in physics-based modelling or numerical simulation.
• Hands-on experience applying machine learning to real physical systems.
• Experience with PyTorch, JAX or other scientific ML frameworks.
• Understanding of numerical methods, boundary conditions and model validation.
• Ability to take research from experimentation through to practical deployment.

Responsibilities

• Develop physics-based models for complex engineering systems.
• Combine traditional numerical solvers with machine learning models to improve speed and accuracy.
• Build and validate scientific ML models against real-world measurements.
• Apply neural operators, surrogate modelling and scientific computing techniques.
• Translate research into reliable tools and workflows used by engineering teams.

Skills

Physics-based modelling

Scientific computing

Machine learning for physical systems

Numerical simulation

Model validation

Experimentation to deployment

Tools

PyTorch

JAX

Job description

Acceler8 Talent is partnering with an early-stage AI company to hire experienced Applied AI Engineers and Researchers in Cambridge, MA. You will develop physics-based models for complex engineering systems and blend numerical solvers with ML to boost simulation speed and fidelity.

You’ll build and validate scientific ML models against real measurements, apply neural operators and surrogate modelling, and translate research into deployable tools used by engineering teams in a hybrid setting (3