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Applied Intuition

Software Engineer - Backend

Sunnyvale$153k–$222kfulltimemidAdded 1 month ago

About this role

Applied Intuition seeks a Backend Software Engineer to design and maintain scalable, low-latency services powering a remote assistance platform for autonomous vehicles. You'll build cloud infrastructure, data pipelines, and APIs handling real-time communication between vehicles and operators across multiple AWS regions.

What you'll do

  • Design and deploy scalable backend services for remote assist session management and real-time data streaming
  • Architect cloud infrastructure using protobuf, gRPC, and multi-region AWS deployment
  • Build data persistence layers with NoSQL databases like Redis
  • Develop data pipelines to ingest, process, and store real-time vehicle data
  • Collaborate with onboard software and front-end teams on end-to-end data flow
  • Optimize latency and bandwidth while ensuring platform security, reliability, and failover resilience

What they're looking for

  • Backend programming (Go, Python)
  • Distributed systems and microservices architecture
  • AWS cloud infrastructure
  • Docker and Kubernetes containerization
  • NoSQL databases (Redis)
  • gRPC and protobuf
  • Real-time data streaming systems
  • API design and development

Benefits

  • Base salary $153,000–$222,000 USD annually
  • Equity (options/RSUs)
  • Comprehensive health, dental, vision, life, and disability insurance
  • 401(k) retirement with employer match
  • Learning and wellness stipends
  • Paid time off
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Applied Intuition

Applied Intuition builds autonomous vehicle and defense systems software, including motion planning algorithms, simulation infrastructure, and autonomy integration platforms for aerial and ground platforms. The company is hiring for security engineers, robotics/autonomy software engineers, hardware-in-the-loop specialists, and IT operations professionals to support its growing physical AI operations.

View all jobs at Applied Intuition

Likely interview questions

  • Describe your experience designing and deploying low-latency backend services. What techniques have you used to optimize performance and minimize latency?
  • Tell us about a time you worked with gRPC and protobuf. How did you handle data serialization and what were the trade-offs you considered?