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Full Stack Developer

Job in Menlo Park, San Mateo County, California, 94029, USA
Listing for: Quality Ai
Full Time position
Listed on 2026-09-05
Job specializations:
  • Software Development
    Backend Developer, DevOps
Salary/Wage Range or Industry Benchmark: 150000 - 170000 USD Yearly USD 150000.00 170000.00 YEAR
Job Description & How to Apply Below
Position: #23653 - Full Stack Developer

#23653 - Full Stack Developer

Date: 31 Aug 2026

Company:
QualityAI

Country/Region: US

Are you interested in working with the World’s leading AI-powered Quality Engineering Company? Ready to advance your career, team up with global thought leaders across industries and make a difference every day? Join us at QualityAI!

We are looking for a Full Stack Developer to join our growing team in Menlo Park, CA.

Location: Menlo Park, CA
Role: Full Stack Developer

Position Overview

We are seeking a strong Full-Stack / Backend Engineer to help design, build, and operate backend services and product surfaces that power internal tools and AI-driven workflows.

This role is best suited for an engineer who is highly practical, execution-oriented, and comfortable working across the stack from backend APIs and infrastructure to user-facing interfaces and mobile surfaces. The ideal candidate has strong hands‑on experience in Python and Type Script and is comfortable deploying modern systems in cloud-native environments. Experience with GenAI systems, orchestration, and production deployment pipelines is highly valuable.

Rust is a strong plus for performance‑sensitive backend work.

Key Responsibilities Core Platform & Agent Operations
  • Working across Phabricator, Code Hub, Tasks, Sheets and Drive; navigating fbsource; tracing a change through its diff history
  • Operating hosted agent control planes — missions and scheduling, the agent-home session registry — and reconciling what they report against what is actually running locally
  • Deep MCP experience — building and running servers and bridges, packaging and distributing plugins, and diagnosing tool‑registry and transport failures
  • Working under agent security guardrails (ASG/SAGE) — understanding a probabilistic LLM‑judge enforcement layer, telling it apart from user authorization, and operating within it rather than around it
  • Applying DSS classification — levels, ceilings, category‑versus‑level enforcement, and recognizing where clearance gates disagree between UI and API
  • Running compliance QA against a written requirement set and producing peer‑comparable evidence
  • Handling security and vulnerability workflows — significant severity, remediation SLOs, coordinating with VM Ops, verifying fixes
  • Dogfooding and red‑teaming internal platforms — filing evidence‑backed defects against the tools you depend on
Design
  • Designing supervisor/worker systems for workers that fail routinely — lifecycle management, health checking, failure isolation, automated recovery, and coordinating concurrent workers over shared state
  • Designing for non‑deterministic agent execution
Build
  • Building every layer of a system yourself — supervisor, tooling and operator UI; breadth matters more than any single language
  • Writing long‑running services that manage child processes — daemon loops, scheduling, timeout budgets, cancellation, graceful shutdown, liveness detection
  • Building operator consoles for at‑a‑glance triage, with as few deployment dependencies as possible
  • Writing test and automation tooling where none exists — browser automation, OS‑level input APIs, screen‑state capture
Plumbing and Integration
  • Writing protocol clients by hand — handshake, timeouts, teardown, and parsing responses that vary in shape
  • Working below the library layer — Unix domain sockets, stdio transports, named pipes, local proxies, connection pooling and keep‑alive behavior
  • Debugging faults that span layers — establishing whether a problem sits in the service, the transport, or the client
  • Handling auth and credentials for local processes — socket‑based auth, certificate agents, token handling, and identity on shared accounts
Maintenance and Operations
  • Owning a running agent fleet day to day — restarts, resets, cadence tuning, scheduled‑job lifecycle, and scheduling against shared or rate‑limited backends
  • Managing unbounded growth — logs and stored data that accumulate until something breaks, and the retention and rotation that prevents it
  • Treating observability as a deliverable — emitting machine‑readable system state and building the views that consume it
  • Diagnosing systems that are degraded but still responding, and writing health checks that test…
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