Senior QA Automation Engineer (AI-First)

Job Description :

Founded by pioneering journalists as the first social media newswire, Storyful was created out of the need to break the news faster and utilize social content to add context to reporting. Acquired by News Corp in 2013, Storyful has evolved into a premium service for media, business leaders and investors.

Our goal is to help our partners understand the attitudes, behaviors and emotions shaping the world. Powered by a unique ability to gather and streamline data from all corners of an increasingly complex information environment, our teams deliver clarity in a world of confusion. Our mission is to dig deeper into the nuance inherent in digital media to establish context, verify the truth and help our partners make sense of the world.

Storyful is building AI-native products on top of complex, high-volume, multi-source content. We are looking for a hands-on Senior QA Automation Engineer to raise the quality bar while increasing delivery speed through AI-augmented engineering.

This is a senior individual contributor role for someone who treats AI as a core part of their craft - using coding agents, LLMs, and evaluation frameworks to scale quality faster than headcount.

You will design and build the automation, evaluation, and quality foundations that enable engineers to own quality, while ensuring our products remain reliable, explainable, and trusted - even as they become increasingly probabilistic.

You will work closely with software engineers, data scientists, product managers, and SRE to embed quality into every stage of the development lifecycle.

What you will do

Design and build AI-augmented automation systems

Build and evolve scalable automation frameworks across web UI, APIs, and data pipelines (Playwright / TypeScript)

Use AI coding agents (e.g. Claude Code) to generate, refactor, and maintain test suites at scale

Translate natural-language acceptance criteria into executable, maintainable tests

Define reusable testing patterns that enable engineers to own automation within their domains

Continuously reduce flakiness using trace analysis, observability, and AI-assisted failure triage

Enable shift-left, engineering-led quality

Partner with engineers to embed testing early in the development lifecycle

Define pragmatic, risk-based test strategies across unit, integration, contract, and E2E layers

Establish clear definitions of done, quality gates, and release readiness standards

Build tooling and frameworks that allow teams to move fast without relying on central QA

Build QA practices for AI/LLM-powered features

Design evaluation pipelines for LLM-driven features using:

Golden datasets and regression harnesses

Semantic similarity scoring

LLM-as-a-judge evaluation patterns

Detect and prevent issues such as prompt drift, hallucinations, retrieval failures, and schema regressions

Test agentic workflows end-to-end (tool use, multi-step reasoning, failure modes)

Partner with data science to define measurable quality signals (precision, recall, grounding, drift)

Integrate quality into CI/CD and delivery

Embed automation and evaluation into CI/CD pipelines for fast, reliable feedback

Define and tune meaningful quality gates based on real metrics (not ceremony)

Support release processes with high-confidence signals and rollback readiness

Drive debugging, observability, and defect excellence

Investigate complex issues across distributed systems using logs, traces, and metrics

Improve observability, including LLM trace visibility (e.g. Langfuse)

Establish strong defect triage, categorisation, and reporting practices

Use data (leakage, flake rate, MTTR, escape rate) to continuously improve quality

Own non-functional and AI-specific risk testing

Embed security testing into automation (OWASP, API security, auth flows)

Test AI-specific risks (prompt injection, jailbreaks, unsafe tool use, data leakage)

Collaborate on performance, resilience, and scalability testing where it matters most

What good looks like in this role

Engineers own quality by default, supported by strong frameworks and AI tooling

Automation is fast, reliable, and trusted as a release signal

LLM-powered features ship with measurable, repeatable quality evaluation

Regression risk is proactively managed across both deterministic and probabilistic systems

Delivery speed increases without compromising trust or stability

Quality becomes a competitive advantage, not a bottleneck

What we’re looking for

Proven experience as a Senior QA Automation Engineer / SDET in production environments

Strong hands-on experience with Playwright (or equivalent) using TypeScript

Demonstrated use of AI coding tools (e.g. Claude Code) as part of daily workflow

Strong software engineering fundamentals and ability to write maintainable production-grade code

Experience designing automation frameworks and test strategies end-to-end

Deep understanding of CI/CD and integrating automation into delivery pipelines

Strong debugging and observability skills across distributed systems

Experience driving shift-left practices and enabling engineers to own quality

Solid understanding of databases, APIs, and test data validation (SQL, REST/GraphQL)

Security-aware mindset (OWASP, secure test design)

Particularly valuable experience

Testing AI/LLM-powered systems (evaluation frameworks, prompt regression, drift detection)

Experience with LLM evaluation patterns (LLM-as-a-judge, semantic scoring, golden sets)

Exposure to RAG pipelines, vector search, or knowledge graph systems

Familiarity with LLM observability and experimentation tools (e.g. Langfuse, MLflow)

Experience testing agentic systems and multi-step workflows

Performance and load testing at scale

Cloud experience (AWS/GCP) and test infrastructure design

Experience in high-trust domains (media, intelligence, risk, compliance)

Why this role matters

This role will define how Storyful delivers quality in an AI-native world.

As our products evolve from deterministic systems to probabilistic, agent-driven platforms, traditional QA approaches no longer scale. This role ensures we can move fast while maintaining trust - building the foundations that allow AI-powered features to work reliably at production scale.

Reasonable Accommodation

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Job Category:

Storyful - Product & Technology

Storyful is a leading reputation intelligence agency for marketing and communications leaders. We leverage proprietary technology, expert analysts, and seasoned strategists to decipher the complexities of the digital landscape. We specialize in measuring and monitoring emerging trends, identifying reputational risks and opportunities, and mapping key spheres of influence. Our mission is to empower our clients to detect, mitigate, and respond effectively to reputational threats and capitalize on brand-building opportunities.