Artificial Intelligence Engineer
LB302
Posted: 04/11/2026
- $180,000 - $300,000
- Cambridge, MA
- Permanent
Member of Technical Staff — Applied AI & Physics
💡 What if your next role let you combine AI, physics and simulation to 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 a Member of Technical Staff, Applied AI (Cambridge, MA — Hybrid).
They're building an AI-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 funding led by a leading climate and deep-tech investor
🧠 Work alongside world-class researchers and engineers from NVIDIA, Google, MIT, Harvard and Carnegie Mellon
⚙️ Solve complex problems at the intersection of AI, physics, simulation and scientific machine learning
🚀 Join a highly technical team of just 27 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.
📩 Interested? Apply now!
Jack Smith
Researcher