Artificial Intelligence Engineer

Acceler8 Talent; Cambridge (MA); Hybrid; USD 120,000 - 180,000; Full time

Job summary

Acceler8 Talent in Cambridge, MA is seeking an Applied AI Engineer to advance physical simulation and digital twins in a hybrid, three-days-per-week role. The position emphasizes building AI-native physics capabilities to tackle AI data centre challenges and support engineering design decisions.

You will blend first-principles simulations with neural models, validate against measurements, and deploy capabilities into customer workflows, turning project work into reusable scientific assets.

Qualifications

• Strong background in engineering or physics with hands-on experience in physical simulations, digital twins, or surrogate models.
• Practical AI fundamentals and applying neural networks to physical problems.
• Master’s degree plus a few years of experience, or a PhD with industry experience.

Responsibilities

• Combine first-principles simulations, solvers, and learned models to explore engineering designs.
• Validate against measurements, investigate discrepancies, and explain model assumptions and limitations.
• Deploy models into customer workflows and turn project work into reusable scientific capabilities.

Skills

Engineering/Physics expertise

AI fundamentals

Neural networks

MS/PhD

Education

Master’s degree in engineering/physics

PhD with industry experience

Tools

PyTorch

JAX

Python

Job description

Applied AI Engineer | Physical Simulation & Digital Twins

Cambridge, MA | Hybrid, three days per week

Physics research at the speed of AI.

This company is building an AI-native physics laboratory where scientists define important problems, AI agents explore potential answers, and rigorous verification determines which results can be trusted.

Its immediate focus is tackling the power and cooling constraints of AI data centres, combining reasoning and simulation to help engineers make better design and operating decisions. The longer-term ambition is to apply the same approach to fundamental scientific discoveries.

What you’ll bring

• Deep expertise in engineering or physics, with hands-on experience building physical simulations, digital twins, or surrogate models.
• Practical AI fundamentals and experience applying neural networks to physical problems.
• A relevant master’s degree plus a few years of experience, or a PhD with industry experience. Strong physical-domain expertise matters more than years in scientific ML.

What you’ll do

• Combine first-principles simulations, solvers, and learned models to explore engineering designs.
• Validate against measurements, investigate discrepancies, and explain model assumptions and limitations.
• Deploy models into customer workflows and turn project work into reusable scientific capabilities.

PyTorch, JAX, neural operators, and uncertainty quantification experience would be helpful.

Eligibility:

Applicants must be US citizens or US permanent residents (Green Card holders)