Full Stack Software Engineer

About Cyngn

Based in Mountain View, CA, Cyngn is a publicly-traded autonomous technology company. We deploy self-driving industrial vehicles like forklifts and tuggers to factories, warehouses, and other facilities throughout North America. To build this emergent technology, we are looking for innovative, motivated, and experienced leaders to join us and move this field forward. If you like to build, tinker, and create with a team of trusted and passionate colleagues, then Cyngn is the place for you.

• We are small and big. With under 100 employees, Cyngn operates with the energy of a startup. On the other hand, we’re publicly traded. This means our employees not only work in close-knit teams with mentorship from company leaders—they also get access to the liquidity of our publicly-traded equity.
• We build today and deploy tomorrow. Our autonomous vehicles aren’t just test concepts—they’re deployed to real clients right now. That means your work will have a tangible, visible impact.
• We aren’t robots. We just develop them. We’re a welcoming, diverse team of sharp thinkers and kind humans. Collaboration and trust drive our creative environment. At Cyngn, everyone’s perspective matters—and that’s what powers our innovation.

Cyngn is building a cloud platform that helps customers monitor, manage, and optimize fleets of autonomous industrial vehicles in real time. As a Full Stack Engineer (Mid–Senior), you’ll ship features end-to-end—from Python backend services to Type Script/JavaScript frontends
—on a small, high-impact team. You’ll partner closely with Staff/Principal Engineers while owning meaningful parts of the product.

If you’re excited by software at the intersection of real-time systems, physical AI, digital twins, and customer-facing product
, we’d love to talk.

• Build customer-facing web experiences: fleet dashboards, live views/maps, alerts, admin tools, and reporting using Type Script/JavaScript.
• Build and evolve backend services in Python that power fleet operations, integrations, data ingestion, and analytics.
• Design and implement reliable APIs (REST and/or gRPC) that are well-documented and easy to integrate with customer systems.
• Deliver real-time features (live vehicle state, events, notifications, operator workflows) using Web Sockets and event-driven patterns.
• Support “physical AI” workflows by connecting cloud software to autonomy/robotics systems—telemetry pipelines, command-and-control surfaces, and operational tooling that interacts with vehicles in the real world.
• Use modern AI tools and agents to move faster and raise quality (and help build customer-facing copilot experiences where it makes sense).
• Contribute to digital-twin simulation + validation loops (where applicable): support workflows that use simulation to test behaviors, validate releases, and reproduce field issues.
• Raise engineering quality through testing, code reviews, observability, and pragmatic reliability/performance improvements.
• Own meaningful chunks of the product: shape solutions, make tradeoffs, and drive work to completion.

• 2–4+ years of professional software engineering experience.
• Strong production experience with:
• Python (backend services, APIs, data workflows)
• Type Script or JavaScript (frontend)
• You’ve shipped and supported user-facing web applications (not just internal tools).
• You’re comfortable building APIs and working with databases (SQL preferred; No
  
  SQL is a plus).
• You communicate clearly, take ownership, and bring a low-ego, collaborative approach.
• You care about software that’s reliable in production, not just “works locally.”

• Real-time systems experience:
  Web Sockets, SSE, streaming updates, pub/sub.
• Event-driven systems / messaging:
  Kafka, Rabbit
  
  MQ, Pulsar, etc.
• Experience with telemetry-heavy or operational products:
  IoT, robotics, autonomy, fleet/dispatch, industrial software.
• Experience building analytics features: reporting, aggregations, operational metrics, customer-facing insights.
• Familiarity with scaling patterns: caching, background jobs, rate limiting, performance tuning.
• Strong habits using AI coding…