Research Software Engineer — Differentiable Scientific Computing (JAX/Julia)

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. Position overview:  We are building AI-assisted simulation and inverse-design tools for electronics, photonics, and semiconductor engineering. We are looking for a scientific-computing engineer with deep experience in JAX, Julia, or other high-performance numerical-computing ecosystems. You will build the computational backbone behind our EDA workflows: differentiable simulation, optimization, uncertainty quantification, large-scale experiment execution, and reproducible numerical pipelines. You will work closely with AI engineers, software engineers, mathematicians, physicists, and domain experts to turn research-grade methods into robust production systems. Open source is an important part of how we build. We contribute upstream when it improves the tools our product depends on, but our primary mission is to deliver reliable, high-performance scientific software for real engineering users. Your mission: Build high-performance scientific-computing systems for simulation, inverse design, optimization, and uncertainty quantification in EDA-related workflows. Develop and optimize JAX and/or Julia-based numerical pipelines, including differentiable solvers, adjoint methods, vectorized workloads, and GPU-accelerated computation. Profile and improve performance across CPU/GPU backends, compilation boundaries, memory movement, batching, sharding, and distributed execution. Build verification infrastructure: numerical regression tests, convergence tests, invariants, benchmark suites, tolerance policies, and reproducible datasets. Translate research prototypes into stable APIs, packages, and production-grade services used by internal teams and customers. Work with open-source ecosystems pragmatically: evaluate libraries, contribute fixes upstream, publish non-differentiating benchmarks or utilities where appropriate. Collaborate with scientists, engineers, and product teams to make advanced computational methods usable in real engineering workflows. Key requirements: 3+ years building scientific, numerical, ML infrastructure, or HPC software in industry, research, open source , or equivalent hands-on work. Deep expertise in JAX or Julia . Strong Python and/or Julia engineering skills, including package design, testing, CI, documentation, and maintainable APIs. Strong understanding of numerical methods , automatic differentiation, optimization, or scientific simulation workflows. Experience profiling and optimizing numerical workloads on CPU and/or GPU. Ability to reason about correctness, reproducibility, numerical stability, and performance trade-offs . Clear communication skills and ability to work across software engineering, AI, mathematics, physics, and product teams. Nice to have: Experience in EDA, photonics, semiconductor design, computational electromagnetics, RF/microwave simulation, or multiphysics simulation. Experience with distributed compute, job schedulers, multi-node workloads, Kubernetes, Docker, cloud infrastructure, or HPC systems. Experience building QA frameworks for scientific or ML systems, including dataset versioning, benchmark dashboards, and numerical regression infrastructure. Experience with lab automation, hardware-in-the-loop systems, PyVISA, SCPI, microcontrollers, FPGAs, or measurement equipment.   What we offer: 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 work. Professional Growth: Opportunities to attend industry conferences, present research findings, and engage with the global AI research community. Impact-Driven Culture: Join a passionate team focused on solving some of the most challenging problems at the intersection of AI and hardware.   Work model & location expectations: Team work model: Hybrid  Primary location: Boston, US   Why join us?  At Axiomatic_AI, you will be working on technology that drives innovation in AI for scientific and engineering applications in line wit