Software Engineer - Full Stack

Reposted One Month Ago
San Francisco, CA, USA
Hybrid
Senior level
Artificial Intelligence • Cloud • Generative AI • Infrastructure as a Service (IaaS)
The Role
Design, build, and maintain a scalable web platform and APIs for deploying and monitoring multimodal AI models and agent workflows. Collaborate with product, infrastructure, and design teams to optimize performance, ensure reliability, drive CI/CD and testing, and contribute to long-term architecture decisions for a cloud-native, multi-tenant SaaS system.
Summary Generated by Built In
About FriendliAI

FriendliAI is the fastest inference cloud for agents, built to run frontier open-weight models at scale in production. It delivers up to 7x faster output token speed, up to 90% lower inference costs, and 99.99% uptime across the most demanding agent workloads — long-context inference, real-time streaming, and accurate tool calling.

We are a small, fast-moving team doing work that matters at one of the most exciting moments in the history of technology. With our world-class inference stack, we are building the platform teams can actually rely on.

About the Role

We're seeking a Full-Stack Software Engineer to design, build, and scale our web platform, which serves as the core interface for deploying multimodal models, observing workloads, and building agent workflows. You'll own how customers experience the platform end-to-end- signing up, authenticating, managing access across an organization, and understanding what they're being billed for - from the services and data models through to the interface. In this role, you'll work closely with product, infrastructure, and design teams to create high-performance, developer-friendly, and enterprise-ready tools.

We are looking for a hands-on engineer who is eager to work across the surfaces of our application — authentication and access control, billing and usage, and the model deployment and inference features customers use every day. The ideal candidate has built backend systems where correctness matters, is comfortable carrying a feature all the way to the UI, cares deeply about developer workflows, and is excited to help define the future of AI adoption.

Key Responsibilities
  • Design, build, and maintain web applications and tools for AI model deployment, monitoring, and performance optimization

  • Own authentication, organization management, and billing end-to-end.

  • Develop clean, scalable, and robust APIs powering AI agents, workflows, and user-facing systems

  • Collaborate with infrastructure engineers to integrate backend systems with deployment and orchestration pipelines

  • Drive code quality through automated testing, CI/CD, and code reviews

  • Contribute to architecture and design decisions that shape our platform's long-term direction

  • Identify and resolve technical debt and improve system reliability in production systems

Qualifications
  • 4+ years of industry experience in backend or full-stack engineering, with meaningful time spent on backend systems

  • Bachelor's or Master's degree in Computer Science, Computer Engineering, or equivalent

  • Production experience with authentication and authorization — SSO/OAuth2/OIDC, RBAC, API keys, and multi-tenant isolation

  • Experience building billing or usage-metering systems: usage-based or subscription pricing, entitlements, quotas, or payment integration

  • Fluent in Python and TypeScript; proficient with React/Next.js

  • Strong backend experience with FastAPI or similar Python frameworks

  • Proficiency in designing data models, writing SQL, and working with PostgreSQL; familiarity with OLAP systems such as ClickHouse

  • Strong API design experience across gRPC/REST/GraphQL in production systems

  • Solid foundation in cloud-native development

  • Familiarity with OpenTelemetry tracing, metrics, and structured logging

Preferred Experience
  • Experience with payment processors (e.g., Stripe) and usage-based or subscription SaaS billing

  • Familiarity with enterprise identity requirements — SAML, SCIM provisioning, or providers like Auth0/WorkOS

  • Familiarity with LLM-based workflows, tool invocation, or agentic systems

  • Familiarity with Kubernetes for container orchestration, including deploying, scaling, and managing containerized applications in production environments

  • Have worked in a startup or fast-paced environment with ownership

  • Built developer-facing SDKs/CLIs

  • Passion for developer experience and enabling AI adoption

Benefits
  • Flexible working hours

  • Daily lunch and dinner provided; unlimited snacks and beverages

  • Supportive and highly collaborative work environment

  • Health check-up support and top-tier equipment/hardware support

  • A front-row seat to the generative AI infrastructure revolution

  • Competitive compensation, startup equity, health insurance, and other benefits.

Skills Required

  • 5+ years of industry experience in full-stack or backend engineering
  • Bachelor's or Master's degree in Computer Science, Computer Engineering, or equivalent
  • Fluent in TypeScript and Python
  • Expert with React and Next.js
  • Strong backend experience with FastAPI or similar Python frameworks
  • Proven expertise delivering production-scale full-stack applications
  • Proficiency in designing data models, writing SQL, and working with PostgreSQL
  • Strong API design experience across gRPC, REST, and GraphQL in production systems
  • Solid foundation in cloud-native development
  • Familiarity with OpenTelemetry tracing, metrics, and structured logging
  • Knowledge of web security, authentication, RBAC, and multi-tenant SaaS systems
  • Familiarity with LLM-based workflows, tool invocation, or agentic systems
  • Familiarity with Kubernetes for deploying and managing containerized applications
  • Experience working in startups or fast-paced environments with ownership
  • Experience building developer-facing SDKs or CLIs
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The Company
34 Employees
Year Founded: 2021

What We Do

FriendliAI is The Frontier AI Inference Cloud: an AI infrastructure platform that deploys, scales, and monitors large language and multimodal models. Its inference engine maximizes GPU utilization to deliver faster performance and steep cost savings for open-weight and custom models, while offering enterprise-grade reliability, SLAs, and compliance to help teams run generative AI and agent workloads at production scale.

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