#23653 - Full Stack Developer

Quality Ai; Menlo Park, Northern (CA, KY); Hybrid; USD 150,000 - 170,000; Full time

Benefits offered by this job

Diversity & inclusion

Internal rotation & global mobility

Career progression

QualityAI Tech academy

Referral bonuses

Competitive pay

Job summary

QualityAI, based in Menlo Park, CA, seeks a strong Full Stack/Backend Engineer to design, build, and operate backend services powering AI-driven workflows. You will work across the stack from APIs to UI, with hands-on Python and TypeScript, and cloud-native deployments.

Rust is a nice-to-have; GenAI experience is highly valued. The role emphasizes practical execution, building across supervisor/worker systems, and delivering secure, scalable platforms that support internal tools and AI-driven

Qualifications

• Strong full-stack/backend engineering experience.
• Proficiency in Python and TypeScript.
• Experience with cloud-native deployments and GenAI systems.

Responsibilities

• Work across Phabricator, CodeHub, Tasks, Sheets and Drive to design and operate backend services.
• Develop supervisor/worker systems with automated recovery and coordinating across workers.
• Build operator consoles for rapid triage with minimal deployment dependencies.
• Ensure security and compliance in agent-centric environments and tooling.
• Evaluate AI-generated code and analysis for accuracy and coverage.

Skills

Python

TypeScript

Rust

Cloud-native

Tools

Phabricator

CodeHub

Sheets

Drive

Job description

#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 TypeScript 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, CodeHub, 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 whether a component can still do its job
• Building integrations that survive dependency upgrades
• Turning incidents into a permanent regression guard
• Evaluating AI‑generated code and analysis critically — spotting unverified claims, stale inputs and silent scope changes, and reading unfamiliar production code well enough to judge whether a fix is genuinely complete
• Writing that carries rationale — code and documentation that explain why, not just what

Languages

Specific languages are less important; relevant experience includes:

• Bash / zsh
• Python
• TypeScript
• JavaScript
• Next.js
• HTML
• C
• AppleScript — useful for quick demos and prototyping

Benefits:

Why QualityAI?
QualityAI is an AI‑first quality engineering company helping enterprises deploy and scale complex systems with greater confidence. Operating across data, models, platforms, infrastructure, and operational environments, the company provides assurance and engineering expertise that helps organizations ensure systems perform reliably in real‑world conditions.

Formerly Qualitest, QualityAI supports global enterprises across regulated and technology‑driven industries, combining deep engineering heritage with AI‑enabled delivery, operational assurance, and lifecycle expertise to help clients achieve certainty at go-live.

• Be a part of a company who strives to support for diversity and inclusion in the workplace - we are one, we are many at QualityAI. Celebrate culture, share knowledge with engineers from around the globe, and inspire each other through our differences.
• Local and global opportunities - we offer you internal rotation and international mobility opportunities to grow your career.
• Clear view of your career and progression with the company - QualityAI is growing massively (since Jan 2021 - added more than 2000 engineers) and giving you the opportunity to grow with us.
• Never stop experimenting and learning with QualityAI Tech academy: 3000+ training courses, mentorship programs, technical tribes, sponsored certifications, leadership programs and much more.
• Earn bonuses via our Client Referral and Employee Referral Program’s. Refer and earn - tap your network for net-worth.
• Competitive pay, the salary range for the role is $150,000 - $170,000.