Engineer, Research Software

As a Research Software Engineer, you will work side-by-side with domain experts in photonics, circuit design, and signal processing to turn scientific code into robust, tested, and scalable infrastructure. You’ll own the schemas, pipelines, and shared software that let our modeling stack grow with the product - making simulations faster to run, results easier to trust, and insights transparent to engineering teams and executive stakeholders alike., * Partner with photonics and circuits engineers on their modeling code - review, refactor, and help evolve prototypes into well-tested, versioned, and properly packaged tools that teams across the company can depend on.

• 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 simulation outputs into trusted inputs for link analysis and yield prediction.
• Scale simulation and analysis workloads across HPC and AWS cloud environments, so link studies and statistical sweeps run reliably and reproducibly at product-relevant scale.
• Integrate AI and ML tooling into engineering workflows - both to accelerate the software development lifecycle and to augment the team’s modeling and data analysis capabilities.
• Bring modern software engineering practice to the group: testing, documentation, CI/CD, reproducible environments, and code review, applied pragmatically in a research setting.
• Collaborate closely with the Photonic Device Design and Analog/Mixed Signal teams to ensure modeling infrastructure evolves with the product roadmap.

• Bachelor’s degree in Computer Science, Data Science, Physics, Optics/Photonics, Electrical Engineering, or a related field.
• 2+ years of experience building scientific or engineering software in a research, product, or modeling context.
• Strong Python skills, including hands-on experience with the scientific stack (NumPy, SciPy, pandas, networkx, or similar).
• Experience owning and maintaining a codebase with real users - internal teams, collaborators, or an external community - through multiple release cycles.
• Experience designing data schemas and APIs used by other engineers or scientists.
• Experience running large-scale compute workloads in AWS and/or HPC environments (SLURM or equivalent).
• Comfortable working on Linux/UNIX systems 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., * Master’s degree or Ph.D.
• 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.
• Working knowledge of at least one of: photonic device physics, circuits, signal processing, or statistical analysis.
• Prior work on compact models, SPICE-like simulators, or link/yield statistical modeling.

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 play a pivotal role in shaping the modeling and simulation backbone behind our silicon photonics optical I/O platform. Our team models our optical links end-to-end. This comprehensive approach spans the physics of individual photonic devices, the circuits and signal processing that drive them, and the statistical analyses used to set product specifications and predict manufacturing yield. To support this, we process large volumes of multi-source measurement and simulation data using a combination of industry-standard and in-house tools.