A
Artificial Intelligence Engineer
Acceler8 Talent
4 hours ago
Full-time
On-site
Cambridge, Massachusetts, United States
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)
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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)
#J-18808-Ljbffr