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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr. Staff Engineer, Modeling Infrastructure and Software Development - **Company:** Ayar Labs - **Location:** San Jose, CA, United States - **Experience:** Expert - **Salary:** $170,000.0 - $223,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Data Analysis, C++ (Programming Language), Software as a Service, Computer Simulation, Linux, Programming Tools, Python (Programming Language), Link Analysis, Metadata Standards, NumPy, Open Source Technology, Software Maintenance, SciPy, Signal Processing, Software Engineering, Technical Data Management Systems, Data Ingestion, Backend, Git, Pandas, Information Technology, Codebase, Data Management, Slurm, Data Lakehouse, Data Pipelines - **Published:** August 10, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=2d5f644eedb26c6d ## About the Role * 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., * 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. ## Description * 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. ## Related Videos - [Running Secure Life Science Research at Scale using Hybrid GPU HPC and Kubernetes 🧬](https://www.wearedevelopers.com/videos/100355-running-secure-life-science-research-at-scale-using-hybrid-gpu-hpc-and-kubernetes) - [Python Data Visualization @ Deepnote (w/ PyViz overview)](https://www.wearedevelopers.com/videos/113-python-data-visualization-deepnote-w-pyviz-overview) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Strange New Worlds: shaping the future of the digital age](https://www.wearedevelopers.com/videos/677-strange-new-worlds-shaping-the-future-of-the-digital-age) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) ## Related Articles - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [Is Software Engineering Hard?](https://www.wearedevelopers.com/magazine/448-is-software-engineering-hard) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)