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Voltai Linkedin · Posted 2mo ago

Senior Software Engineer

Canada

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Indexed description

This is a work from office role at our Bay Area office near Menlo Park.

The Role

You'll design, build, and ship core systems that power our AI platform. You'll work across the stack—backend services, infrastructure, APIs, and product features—and have real ownership over what you build. Expect to move between greenfield work, hardening existing systems, and to have your fingerprints on architecture decisions that matter.

About Voltai

Voltai is developing models and agents to evaluate, design, and interact with hardware and electronic systems.

The Team

We're hackers, researchers, and operators obsessed with building AI for electronic systems and semiconductors. Our team includes former Stanford professors, SAIL researchers, Olympiad medalists, and executives from Synopsys, GlobalFoundries, and Cadence. Despite the name drops, we care far more about execution than titles.

We're backed by Stanford, Sequoia, and leadership at Google, AMD, Broadcom and more. In other words, we have deep, deep access to the industry.

What You'll Do

  • Ship features end-to-end working closely with research and product.
  • Improve foundations: performance, reliability, observability, developer velocity.
  • Raise the engineering bar through code review ad design discussions.
  • Learn about hardware design. Previous experience is not expected, but you should be eager to familiarize yourself with how hardware is developed.

What We're Looking For

  • 5+ years building production software. Internships and teaching assistantships don't count.
  • Strong knowledge of agent development, evaluations
  • Fluency in at least one mainstream backend language (Python, Go, Rust, TypeScript, etc.) and familiarity with either Python or TypeScript
  • A track record of owning meaningful systems or features and not just contributing to them.

Bonus Points For

  • Experience in EDA, semiconductor, or hardware-adjacent software environments
  • Background in ML infrastructure, model training, or model serving
  • Experience at an early-stage startup where you wore multiple hats

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