Research Software Engineer (Scientific Computing)

TL;DR

Research Software Engineer (Scientific Computing): Build and scale computational backbone of scientific tools for simulation, inference, optimization, and uncertainty quantification workflows with an accent on electromagnetic simulation and inverse design. Focus on performance optimization in end-to-end pipelines, distributed execution for large experiments, and rigorous verification practices ensuring correctness and reproducibility.

Location: Hybrid (2-3 days/week) in Barcelona, Spain or Boston, US

Company

Building a new class of AI systems combining deep learning, formal logic, and physics-based modeling to accelerate semiconductor and photonic hardware development.

What you will do

• Build performant systems for simulation, inference, optimization, and uncertainty quantification, focusing on electromagnetic simulation and inverse design.
• Profile and optimize performance in CPU/GPU end-to-end pipelines.
• Design distributed compute infrastructure for multi-GPU/multi-node experiments with reproducibility and observability.
• Implement testing and verification for scientific pipelines including numerical regression and convergence tests.

Requirements

• Master’s degree in Computer Science, Data Science, AI, Physics or related
• 2+ years industry experience as software engineer; scientific computing, HPC, ML infra, or performance engineering preferred
• Expert Python proficiency
• Track record building complex systems prioritizing correctness, performance, reliability
• Experience with performance profiling/optimization for numerical workloads (CPU/GPU)
• Strong numerical computing knowledge; cloud platforms and containerization (Docker, Kubernetes)
• Strong communication skills; self-motivated with proactive mindset

Nice to have

• Proficiency in JAX/XLA, Julia, C++/CUDA, MPI
• Custom kernels or XLA/JAX optimization; profiling tools
• QA frameworks for scientific/ML systems
• Distributed systems for compute-heavy workloads
• Open-source contributions in scientific computing or ML infra

Culture & Benefits

• Competitive compensation and stock options
• Access to cutting-edge tools and collaboration with AI, physics, hardware experts
• Flexible hybrid work in offices with potential remote options
• Professional growth: conferences, research presentations, global AI community
• Impact-driven culture solving AI-hardware challenges