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