Research Software Engineer — Scientific Computing Systems

Research Software Engineer — Scientific Computing Systems

About us: Axiomatic AI is building a new class of AI systems designed to reason with the rigor of the scientific method. By combining deep learning with formal logic and physics-based modeling, we create verifiable, interpretable AI systems that collaborate with and support human researchers in high‑stakes scientific and engineering workflows.

Our mission, 30×30
, is to deliver a 30× improvement in the speed, accessibility, and cost of semiconductor and photonic hardware development by 2030.

We aim to revolutionize hardware design and simulation in these industries and are building a team of highly motivated professionals to bring these innovations from research into commercial products.

As a Research Software Engineer specialized in scientific computing systems, you will build and scale the computational backbone of our scientific tools: accelerating simulation and optimization workloads, enabling robust distributed execution, and ensuring correctness and reproducibility across numerical pipelines. You will work at the intersection of numerical computing, performance engineering, and platform reliability to bring research‑grade methods into production. Close collaboration with our cross‑functional team, consisting of AI Engineers, Software Engineers, Mathematicians, and Physicists, will be essential to deliver tools that transform how engineers and scientists use AI in their day‑to‑day work.

• Scalable Scientific Computing: Build performant, reliable systems for simulation, inference, optimization, and uncertainty quantification workflows, especially for electromagnetic simulation and inverse design.
• Performance Optimization: Profile and optimize performance in end‑to‑end pipelines (CPU/GPU).
• Distributed Execution & HPC: Design and maintain distributed compute infrastructure for large sweeps and multi‑run experiments (multi‑GPU/multi‑node), focusing on reproducibility, observability, and developer ergonomics.
• Verification & QA: Implement rigorous testing and verification practices for scientific computing pipelines (numerical regression, in variants, convergence tests, golden datasets), ensuring trustworthy results over time.

• Master’s degree in Computer Science, Data Science, Artificial Intelligence, Physics or related field.
• 2+ years of relevant industry experience as a software engineer; experience in scientific computing, HPC, ML infrastructure, or performance engineering strongly preferred.
• Expert‑level proficiency in Python.
• Strong track record building and shipping complex systems where correctness, performance, and reliability are first‑class concerns.
• Experience with performance profiling/optimization for numerical workloads (CPU and/or GPU) and comfort navigating low‑level bottlenecks.
• Strong understanding of numerical computing performance considerations.
• Experience with cloud computing platforms and containerization (Docker, Kubernetes).
• Strong communication skills with ability to explain technical concepts to diverse audiences.
• Self‑motivated with a proactive, solution‑oriented mindset and ability to lead technical initiatives.

• Strong proficiency in relevant scientific computing ecosystems (JAX/XLA, Julia, C++/CUDA, MPI, …).
• Experience writing custom kernels or optimizing XLA/JAX pipelines; familiarity with profiling tools.
• Experience designing QA frameworks for scientific/ML systems (numerical regression, dataset versioning, KPI dashboards).
• Experience building, operating, or integrating distributed systems for compute‑heavy workloads (e.g., job schedulers, multi‑node training/simulation, or large‑scale batch pipelines).
• Contributions to open‑source scientific computing or ML infrastructure projects.

• Competitive compensation
• Stock Options Plan: Empowering you to share in our success and growth.
• Cutting‑Edge Tools: Access to state‑of‑the‑art tools and collaborative opportunities with leading experts in artificial intelligence, physics, hardware and electronic design automation.
• Work‑Life Balance: Flexible work arrangements in one of our offices with potential options for remote…