Member of Technical Staff - Applied AI

Acceler8 Talent; Cambridge (MA); Hybrid; USD 120,000 - 190,000; Full time

Benefits offered by this job

Hybrid work model

Job summary

Acceler8 Talent is partnering with an early-stage AI company in the United States that applies machine learning and physics-based modelling to tackle complex engineering challenges. They seek experienced Applied AI Engineers and Researchers who can fuse scientific computing with ML to build robust systems.

Location: Cambridge, MA, hybrid with three days on-site. The role offers opportunities at the AI-physics frontier, collaborating with researchers and engineers on real-world problems in

Qualifications

• 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.

Responsibilities

• 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.

Skills

Physics-based modelling

Numerical simulation

Scientific computing

Applied machine learning

Tools

PyTorch

JAX

SciPy

Job description

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)

Interested in learning more? Apply directly or send me a message for a confidential discussion.