Member of Technical Staff - Applied AI

Acceler8 Talent; Massachusetts; Hybride; USD 120 000 - 160 000; Plein temps

Résumé du poste

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.

Responsabilités

• 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.

Connaissances

Physics-based modelling

Scientific computing

Machine learning for physical systems

Numerical simulation

Model validation

Experimentation to deployment

Outils

PyTorch

JAX

Description du poste

I'm working with an early-stage AI company applying machine learning and physics-based modelling to complex engineering challenges.

They're looking for experienced Applied AI Engineers and Researchers who can combine scientific computing, physical modelling and machine learning to develop systems that solve real-world engineering problems.

The role:

• Develop physics-based models for complex engineering systems
• Combine traditional numerical solvers with machine learning models to improve simulation 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

What they're looking for:

• Strong background in physics-based modelling, numerical simulation or scientific computing
• 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

Why consider it?

• An opportunity to work at the intersection of AI, physics and engineering, developing technology that can transform how complex physical systems are designed, simulated and optimised.
• You'll work alongside researchers and engineers on challenging problems, with initial applications in AI data-centre infrastructure and engineering.

Location:

Cambridge, MA | Hybrid (3 days onsite)