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Sr. Staff Engineer, Modeling Infrastructure and Software Development
Ayar Labs
San Jose, USA
Full-time
Senior Level
Onsite
USD 170000-223000 yearly
Discovered 1 weeks ago
Free
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- 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.
- 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.
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