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Computational Simulation Engineer (AI-Accelerated)

CircuitLeap.ai

CircuitLeap.ai

Software Engineering, Data Science
Posted on Oct 16, 2025

Computational Simulation Engineer (AI-Accelerated)

Location: Remote or Hybrid in Lisbon, Portugal

CircuitLeap is pioneering the future of analog circuit simulation by combining traditional numerical methods with cutting-edge AI acceleration. We're seeking a Computational Simulation Engineer who can bridge the gap between classical physics-based solvers and modern machine learning techniques to create the next generation of ultra-fast, accurate simulation tools for the semiconductor industry.

Responsibilities

  • Develop and optimize numerical simulation algorithms for analog circuit analysis, leveraging both traditional techniques and AI acceleration.
  • Design and implement finite element analysis (FEA) solvers for multi-physics problems in semiconductor devices.
  • Train and deploy machine learning models that can act as surrogate simulators, achieving orders of magnitude speedup while maintaining accuracy.
  • Create hybrid simulation frameworks that intelligently combine physics-based solvers with AI models for optimal performance.
  • Collaborate with circuit designers to understand simulation bottlenecks and develop targeted AI-accelerated solutions.
  • Optimize computational kernels for GPU acceleration using CUDA, ensuring maximum throughput for large-scale simulations.
  • Validate simulation accuracy against industry-standard tools and real-world measurement data.

Required Qualifications

  • MS or PhD in Computational Physics, Applied Mathematics, Electrical Engineering, or related field with focus on numerical methods.
  • Strong foundation in numerical simulation techniques including finite difference, finite element, and finite volume methods.
  • Hands-on experience with finite element analysis (FEA) for solving PDEs in engineering applications.
  • Proven expertise in training AI/ML models for scientific computing applications, particularly physics-informed neural networks (PINNs) or similar approaches.
  • Proficiency in Python and scientific computing libraries (NumPy, SciPy, PyTorch/TensorFlow).
  • Experience with parallel computing and numerical optimization techniques.
  • Strong mathematical background in linear algebra, differential equations, and numerical analysis.

Preferred Qualifications

  • Experience with electrical and thermal solvers for semiconductor device simulation (TCAD, SPICE, heat transfer).
  • Knowledge of electromagnetic field solvers and Maxwell's equations discretization.
  • Familiarity with model order reduction techniques and surrogate modeling.
  • Experience with GPU programming (CUDA, OpenCL) for accelerating numerical algorithms.
  • Background in semiconductor physics and understanding of device-level phenomena.
  • Publications or patents in computational simulation or AI-accelerated solvers.
  • Experience with commercial simulation tools (COMSOL, ANSYS, Cadence Spectre).

Why This Role Matters

  • Lead the transformation of analog circuit simulation from hours to seconds using AI.
  • Develop technology that will fundamentally change how engineers design and optimize circuits.
  • Work at the intersection of physics, mathematics, and artificial intelligence.
  • Directly impact the speed of innovation in the semiconductor industry.
  • Shape the architecture of next-generation simulation platforms used by thousands of engineers.

What We Offer

  • Opportunity to work on groundbreaking technology combining classical physics with modern AI.
  • Access to state-of-the-art GPU clusters and computational resources.
  • Collaboration with world-class researchers and engineers in simulation and AI.
  • Competitive compensation with significant equity participation.
  • Flexible work arrangements and a culture that values innovation and technical depth.
  • Clear path to technical leadership as we scale our simulation platform.

Apply Now

Ready to revolutionize computational simulation with AI? Please complete our application form:

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