Sr. Staff Engineer, Modeling Infrastructure and Software Development

Ayar Labs•San Jose, CA

2d•Onsite

About The Position

Ayar Labs is shattering AI data bottlenecks by moving data at the speed of light. As pioneers of co-packaged optics (CPO), we are using light instead of electricity to move data faster, further, and with a fraction of the energy needed to fuel the explosive growth of AI models. Backed by industry giants like NVIDIA, AMD, Mediatek and Intel and manufactured in partnership with the world's leading semiconductor ecosystem, Ayar Labs' co-packaged optics solution is key to unleashing next-generation AI scale-up architectures. Joining our Link Design and Architecture team, you will be the software and compute backbone behind our silicon photonics optical I/O platform. Our team models optical links end-to-end — from the physics of individual photonic devices, through the circuits and signal processing that drive them, to the statistical analyses that set product specifications and predict manufacturing yield. The team's engineers are domain experts in photonics, circuits, and signal processing who write serious numerical Python; your job is to make their code, data, and compute reliable, reproducible, and scalable. This is a software and data infrastructure role embedded in a modeling team — it is not a web services or SaaS backend position. You won't be building the models themselves; you'll be building the compute, data, and software foundation the team's engineers rely on.

Requirements

Degree in Computer Science, Physics, Electrical Engineering, Optics/Photonics, or a related field; advanced degree a plus.
5+ years building and maintaining software and compute infrastructure for numerical simulation, engineering modeling, or large-scale technical data analysis, for example in semiconductor/EDA, aerospace, a national lab, or a similar technical computing environment.
Enterprise web/SaaS backend experience alone is not a fit.
Strong Python skills with the scientific stack (NumPy, SciPy, pandas, networkx, or similar), applied to numerical or data-analysis problems — not only service code.
A track record of owning a codebase used by engineers or scientists, i.e. internal tooling or open-source, through multiple release cycles.
Hands-on operation of batch compute at scale: SLURM (or an equivalent HPC scheduler) and/or AWS-based scientific compute, including environment management, containers, and CI for computational workloads.
Experience designing data schemas and APIs consumed by other engineers or scientists.
Fluency on Linux and with modern developer tooling (Git, CI, containers).
Excellent communication skills and a collaborative working style, with the ability to engage deeply with domain experts across disciplines.

Nice To Haves

Working knowledge of at least one of: photonic device physics, circuits, signal processing, or statistical analysis.
Contributions to open-source scientific software.
Experience with modern data management architectures (e.g., data lakehouse patterns), metadata standards, and query engines.
Hands-on experience applying AI tools and agentic workflows to engineering or data pipelines.
Proficiency in C/C++ or another compiled language used alongside Python in scientific codebases.
Prior work on compact models, SPICE-like simulators, or link/yield statistical modeling.

Responsibilities

Own the team's software development and compute environment: reproducible Python environments, containerized workflows, CI/CD, data standards, and versioned releases of internal modeling packages.
Operate and scale simulation and statistical analysis workloads across HPC (SLURM) and AWS environments, so link studies and yield sweeps run reliably and reproducibly at product-relevant scale.
Design and maintain the data schemas and interfaces that describe devices, measurements, model parameters, and simulation results across the modeling stack.
Build and operate data ingestion, cleaning, and curation pipelines that turn raw measurement and foundry data into trusted, queryable inputs for link analysis and yield prediction.
Partner with photonics, circuits, and signal-processing engineers to evolve research prototypes into well-tested, packaged tools that teams across the company depend on, bringing modern software practice to a research setting pragmatically, not dogmatically.
Integrate AI and agentic tooling into engineering workflows, both to accelerate the software development lifecycle and to augment the team's modeling and data analysis capabilities.