Build scalable, resilient backend microservices in Go; design gRPC/REST APIs; containerize with Docker; deploy and manage services on Kubernetes; implement asynchronous messaging (NATS/RabbitMQ); work with PostgreSQL; drive CI/CD, testing, code reviews, and documentation for greenfield robotics orchestration systems.
At Kerrigan Robotics, we aren’t just adding more "iron" to the factory floor; we’re building the intelligence that brings it all to life. As an AI-powered, robot-agnostic orchestration platform, we bridge the gap between high-level software and complex physical hardware, harmonizing everything from humanoids and drones to legacy ERP and WMS systems. Our team’s pedigree is rooted in scaling the impossible, having first proven our mettle at Tesla by scaling $5B worth of equipment and thousands of robots globally before achieving seamless multi-branded integration for Rivian’s fleet and lineside controls. While the rest of the industry focuses on building individual robots, we are operationalizing heterogeneous automation at scale slashing production ramp-up times by 70% and making enterprise level expansion feel effortless. If you’re ready to turn fragmented data into harmonious communication and help make the future of manufacturing simpler, more reliable, and truly unified, your next challenge starts here.
The Role
We are looking for a Generalist Backend Engineer who thrives on building scalable, resilient
distributed systems. In this role, you won’t just be “writing APIs”; you will be building the
backbone of an automation platform that manages high-stakes robotics workloads. You are a
pragmatic coder who values containerization, orchestration, and clean system design.
Key Responsibilities
● Service Development: Design and implement performant microservices in Golang.
● Orchestration: Deploy and manage services within Kubernetes, ensuring high availability
and proper resource management.
● System Integrity: Utilize Docker for consistent environment packaging and NATS or
similar for asynchronous messaging.
● API Design: Build clean, versioned APIs (gRPC/REST) that serve both our frontend and
external integration partners.
● Maintainability: Actively participate in code reviews and contribute to a culture of
automated testing and documentation.
Requirements
● 5+ years of experience in backend development with a focus on distributed systems.
● Core Stack: Professional experience with Go (Golang) in a production environment.
● Cloud Native: Strong proficiency with Docker and Kubernetes.
● Infrastructure Mindset: Familiarity with modern databases (PostgreSQL) and message
brokers (NATS, RabbitMQ).
● Tooling: Experience with CI/CD pipelines and Git-based workflows.
What This Role Is Not
● This is not a frontend or full-stack role; the focus is 100% on backend systems and
infrastructure.
● This is not a "maintenance-only" role; you will be building new greenfield services.
The base pay range for this role is $140,000 – $205,000 per year.
Similar Jobs
Artificial Intelligence • Information Technology • Machine Learning • Natural Language Processing • Productivity • Software • Generative AI
Build and operate high-throughput backend services powering AI agent orchestration, connector integrations, and real-time data pipelines. Design scalable core platform services, collaborate with ML, frontend, and product teams, implement infrastructure-as-code and CI/CD, and mentor junior engineers to deliver reliable, AI-powered features.
Top Skills:
Agent OrchestrationAWSAzureCi/CdClaude CodeConnector IntegrationsCopilotCursorEvent-Driven ArchitectureGCPHigh-Throughput Api DesignInfrastructure As CodeReal-Time Data PipelinesReal-Time Systems
Automotive • Big Data • Information Technology • Robotics • Software • Transportation • Manufacturing
Design, develop, and maintain large-scale backend systems and APIs. Lead technical design and implementation of distributed, service-oriented and event-driven architectures. Perform code reviews, ensure security and performance, troubleshoot complex production incidents, and lead root-cause analysis and remediation. Hybrid role with 3 days in-office and ~20% domestic travel.
Top Skills:
Asynchronous MessagingAuthenticationAuthorizationAutomated TestingAWSAzureCi/Cd PipelinesContainerized EnvironmentsData ModelingDeployment PracticesEvent-Driven SystemsGCPJavaKotlinMicroservicesNoSQLRelational DatabasesRestful ApisService-Oriented Architecture
Cloud • Computer Vision • Information Technology • Sales • Security • Cybersecurity
Lead design and implementation of high-scale cloud backend authentication/authorization services. Build and operate event-driven microservices, mentor engineers, improve architecture and reliability, and contribute to open source. Focus on AuthN/AuthZ, fine-grained access control, scalability, and production ownership.
Top Skills:
AWSC# (.Net Core)CassandraElasticsearchGitGoGraphQLJavaKafkaNode.jsOauthOpenfgaPostgresPythonRedisScalaZanzibarZmq
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
