Artificial Intelligence Engineer

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

Member of Technical Staff — Applied AI & Physics

What if your next role let you combineAI, physics and simulationto solve some of the hardest engineering challenges facing the next generation of AI infrastructure?

Our client — an exceptionally well-funded AI research startup — is hiring aMember of Technical Staff, Applied AI(Cambridge, MA — Hybrid).

They're building anAI-native physics laboratory, combining machine learning, scientific simulation and first-principles modelling to tackle real-world physical challenges, starting with the power and cooling constraints of AI data centres.

The role:

• 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

Why join?

$58M seed fundingled by a leading climate and deep-tech investor

Work alongside world-class researchers and engineers fromNVIDIA, Google, MIT, Harvard and Carnegie Mellon

Solve complex problems at the intersection ofAI, physics, simulation and scientific machine learning

Join a highly technical team of just27 people, with significant ownership from day one

Help build an AI-native physics research platform with ambitions extending beyond data-centre optimisation

Work directly with experienced scientific and technical leadership in a high-ownership research environment

Cambridge, Massachusetts — Hybrid (3 days onsite)

We're looking for engineers or researchers with strong backgrounds in physics, mechanical engineering, scientific computing or related disciplines.

Experience building physical simulations or digital twins is particularly valuable. Deep expertise in scientific ML isn't essential if you have strong physical-domain knowledge and practical AI experience.