About the role
structured by ORI← All open roles ### AI and Data Infrastructure Engineer Full-time · On site, San Francisco ### Summary of position We are building the optical layer that the next generation of AI and quantum infrastructure will run on. Our engineers generate fabrication, measurement and simulation data every day.
What you will do
- Design versioned schemas for runs, samples, instruments, calibration, conditions and uncertainty.
- Build ingestion for lab and simulation workflows. Keep raw data immutable and derived data traceable.
- Make every result reproducible from the original measurements by another engineer.
- Build AI-assisted search that cites source records and flags missing or conflicting data.
- Provide browsing and analysis tools. Run numerical analysis through deterministic, tested code.
What they are looking for
- Degree in physics, computer science, engineering or a related field. A PhD is not required.
- Physics literacy and experience reasoning about experimental data.
- Strong Python and experience building reliable data pipelines.
- A data system you personally designed and operated, with real users.
- Data modeling, schema evolution, testing and provenance.
- Practical experience with AI and retrieval systems.
- Clear written and verbal communication, fluent English.
Nice to have
- Data infrastructure in high-energy physics, materials science or another data-heavy scientific field.
- Data from specialized lab instruments, especially in optics and photonics.
- Simulation data management.
- Cloud infrastructure and databases.
- Startup experience.
Benefits
- Equity and benefits available, to be agreed upon.
Full posting text
← All open roles
AI and Data Infrastructure Engineer
Full-time · On site, San Francisco
Summary of position
We are building the optical layer that the next generation of AI and quantum infrastructure will run on. Our engineers generate fabrication, measurement and simulation data every day. As AI and Data Infrastructure Engineer, you will build the platform that turns this data into reproducible engineering decisions. You will design how data is captured, stored, traced and queried, and add AI-assisted retrieval that always cites its sources.
You will work side by side with physicists and engineers in the lab. If you like building systems that scientists actually trust and use, you'll fit well here.
What you'll do
- Data model. Design versioned schemas for runs, samples, instruments, calibration, conditions and uncertainty.
- Pipelines. Build ingestion for lab and simulation workflows. Keep raw data immutable and derived data traceable.
- Provenance. Make every result reproducible from the original measurements by another engineer.
- AI retrieval. Build AI-assisted search that cites source records and flags missing or conflicting data.
- Analysis tools. Provide browsing and analysis tools. Run numerical analysis through deterministic, tested code.
- Operations. Own access control, backups, schema migration and correction history.
- Cross-team work. Work with process, test, device and optics teams to capture what each measurement means.
What we are looking for
- Degree in physics, computer science, engineering or a related field. A PhD is not required.
- Physics literacy and experience reasoning about experimental data.
- Strong Python and experience building reliable data pipelines.
- A data system you personally designed and operated, with real users.
- Data modeling, schema evolution, testing and provenance.
- Practical experience with AI and retrieval systems.
- Clear written and verbal communication, fluent English.
Nice to have
- Data infrastructure in high-energy physics, materials science or another data-heavy scientific field.
- Data from specialized lab instruments, especially in optics and photonics.
- Simulation data management.
- Cloud infrastructure and databases.
- Startup experience.
Compensation
- Competitive salary commensurate with experience.
- Equity and benefits available, to be agreed upon.
Apply now