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.

Equal Opportunity Employer

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, age, national origin, protected veteran status, disability status or any other protected characteristic under applicable law.

Reasonable Accommodation

We are committed to providing reasonable accommodation for qualified individuals with disabilities in our job application and/or interview process. If you need assistance or accommodation in completing your application or participating in an interview due to a disability, email us at talentresourceteam@dowjones.com. Please put "Reasonable Accommodation" in the subject line and provide a brief description of the type of assistance you need. This inbox will not be monitored for application status updates.

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Business Area:

Dow Jones - Risk

Job Category:

Software Product Engineering

Union Status:

 

Base Pay Range: -

We’re committed to offering competitive and flexible compensation to attract top talent. This pay range reflects our good faith estimate for the role and may vary based on a candidate’s experience, skills, location, and other relevant factors.

For bonus-eligible roles, targets are determined based on multiple considerations, including market benchmarks and individual contributions.

For benefits-eligible roles, we offer a comprehensive and competitive benefits package covering health, retirement, wellbeing, and more, along with optional benefits to meet the diverse needs of our employees.