Applied AI Engineer — Physics, Simulation & Digital Twins

Acceler8 Talent; Massachusetts; Hybrid; USD 180,000 - 240,000; Full time

Job summary

Acceler8 Talent is seeking a Member of Technical Staff, Applied AI, in Cambridge, MA. The role blends AI, physics, and simulation to tackle the engineering challenges of AI infrastructure. Join a well-funded lab building AI-native physics platforms with high ownership from day one.

The position requires building simulations and digital twins, combining solvers with neural networks, and validating models with real measurements. Hybrid work (3 days onsite) in Cambridge, MA.

Qualifications

• Build simulations, digital twins and surrogate models of complex physical systems.
• Combine physics-based solvers with neural networks and machine learning approaches.
• Develop AI-driven models to optimise engineering systems and infrastructure.
• Validate simulations against real-world measurements, physical constraints and experimental data.
• Work across scientific computing, applied ML and engineering research.
• Own technical projects end-to-end, from initial research through to real-world deployment.

Responsibilities

• Build simulations, digital twins and surrogate models of complex physical systems.
• Combine physics-based solvers with neural networks and machine learning approaches.
• Develop AI-driven models to optimise engineering systems and infrastructure.
• Validate simulations against real-world measurements, physical constraints and experimental data.
• Work across scientific computing, applied ML and engineering research.
• Own technical projects end-to-end, from initial research through to real-world deployment.

Job description

Acceler8 Talent is seeking a Member of Technical Staff, Applied AI, in Cambridge, MA. The role blends AI, physics, and simulation to tackle the engineering challenges of AI infrastructure. Join a well-funded lab building AI-native physics platforms with high ownership from day one.

The position requires building simulations and digital twins, combining solvers with neural networks, and validating models with real measurements. Hybrid work (3 days onsite) in Cambridge, MA.