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Security Engineer – Product Security

AI Summary
Spotify is seeking a Product Security Engineer to build agentic-AI workflows that convert vulnerability findings into safe, tested, review-ready code changes. The role owns tooling and evaluation frameworks for measuring correctness, safety, build and test success, and merge outcomes of automated remediation. It combines secure software engineering, vulnerability management, AI-assisted development, and collaboration with developers and security stakeholders. The position is based around Toronto with work-from-home flexibility and some required in-person meetings. Success depends on improving remediation reliability at scale while balancing security risk, feasibility, and developer experience.
Remote from
USA, Canada
Salary
Undisclosed
Department
Cybersecurity
Employment
Full Time
Experience
Open level
Published
Apply before
6 Dec 2026
Listing views
36
Application actions
2
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Opportunity details

About this role.

The Platform team creates the technology that enables Spotify to learn quickly and scale easily, enabling rapid growth in our users and our business around the globe. Spanning many disciplines, we work to make the business work; creating the infrastructure, tooling, frameworks, and capabilities needed to welcome a billion customers.

Security engineers at Spotify help protect our environment and more than 700 million users. Within Platform, our Product Security team evaluates and communicates Spotify’s security posture, helping developers and leadership make informed decisions about security risks. We own the vulnerability management platform, supporting the full lifecycle of identifying, attributing, remediating, and reporting security vulnerabilities.

As AI changes how vulnerabilities are discovered and exploited, we’re evolving how Spotify responds. We’re looking for a Security Engineer to help transform vulnerability findings into reliable, actionable code improvements using agentic AI. You’ll build the tools, workflows, and evaluation frameworks that make automated remediation more effective, scalable, and safe, while helping developers focus on building great experiences for our users.

What You’ll Do

  • Build and improve agentic AI workflows that turn security vulnerability findings into tested, review-ready code changes for engineering teams across Spotify.

  • Design and implement evaluation frameworks to measure the quality of automated fixes, including correctness, completeness, safety, build and test success, and pull request merge rates.

  • Develop tools and processes to compare remediation approaches, evaluate AI models, and continuously improve the reliability and effectiveness of generated fixes.

  • Research and apply emerging practices in AI-assisted vulnerability remediation, including prompt engineering, context engineering, and vulnerability-specific fix guidance.

  • Identify which vulnerability types are suitable for automated remediation, balancing security impact, technical feasibility, and developer experience.

  • Collaborate with developers and security stakeholders to collect feedback through code reviews, pull requests, support channels, and interactive sessions, using these insights to improve remediation quality.

  • Help shape how Spotify approaches security posture management by building scalable solutions that reduce security risks and make it easier for teams to address vulnerabilities.

Who You Are

  • You have experience in software engineering, particularly within security engineering, developer experience, or internal infrastructure.

  • You can confidently read, understand, and write code in one or more languages such as Java, Python, Scala, C++, or TypeScript.

  • You have experience designing and building scalable systems, with an understanding of software architecture and reliable engineering practices.

  • You have worked with agentic AI systems or AI-assisted development workflows and understand how to apply them to real-world engineering challenges.

  • You understand common security risks, attack vectors, and vulnerabilities, including those affecting AI and machine learning systems, and approaches to mitigating them.

  • You have experience integrating security tools or automated workflows into production engineering environments.

  • You are familiar with agentic AI frameworks and approaches to evaluating the quality, reliability, and safety of AI-generated outputs.

  • You enjoy collaborating across teams, learning from feedback, and navigating ambiguity. You’re comfortable experimenting with new approaches while balancing innovation with thoughtful security decisions.

Where You’ll Be

  • This role is based in Toronto

  • We offer you the flexibility to work where you work best! There will be some in person meetings, but still allows for flexibility to work from home.

Compensation

Additional Information

Spotify is an equal opportunity employer. You are welcome at Spotify for who you are, no matter where you come from, what you look like, or what’s playing in your headphones. Our platform is for everyone, and so is our workplace. The more voices we have represented and amplified in our business, the more we will all thrive, contribute, and be forward-thinking! So bring us your personal experience, your perspectives, and your background. It’s in our differences that we will find the power to keep revolutionizing the way the world listens.

At Spotify, we are passionate about inclusivity and making sure our entire recruitment process is accessible to everyone. We have ways to request reasonable accommodations during the interview process and help assist in what you need. If you need accommodations at any stage of the application or interview process, please let us know – we’re here to support you in any way we can.

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This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

4/5
RelaxedFast-paced

Autonomy Level

4/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightЭто сложная инженерная роль на стыке продуктовой безопасности, масштабируемых платформ и агентных AI-систем. Нужно обеспечивать безопасность и воспроизводимую оценку автоматически сгенерированных исправлений в крупной production-среде.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Highly competitive
$220,000
US market range (per year)$190k–$250k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. Работодатель не раскрыл базовую компенсацию. Указана ориентировочная годовая рыночная оценка в USD для опытного инженера по продуктовой безопасности с AI-направлением; период выбран как логичный для постоянной профессиональной роли.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you evaluate whether an AI-generated vulnerability fix is safe to merge?

I would use layered evaluation: verify that the vulnerability is addressed, run unit and integration tests, add security-focused regression tests, assess code quality and scope of changes, and require human review for higher-risk categories. I would also track production signals and rollback outcomes to improve the evaluation suite over time.

How would you decide which vulnerability types are suitable for automated remediation?

I would prioritize high-volume, well-understood findings with deterministic remediation patterns, strong test coverage, and limited blast radius. I would exclude or gate findings involving authentication, authorization, cryptography, sensitive data flows, or architectural changes until the evaluation evidence supports safe automation.

Describe an evaluation framework for comparing two AI remediation approaches.

I would create a representative, versioned benchmark of real vulnerability cases and score each approach on vulnerability resolution, functional correctness, test and build success, patch minimality, security regressions, reviewer acceptance, and merge rate. Results should be segmented by language, repository type, and vulnerability class to identify where each approach performs reliably.

How would you integrate automated remediation into an existing engineering workflow?

I would start with a low-risk pilot that creates pull requests rather than automatically merging changes. The workflow should include clear findings context, suggested fixes, test evidence, confidence indicators, ownership routing, audit logs, and feedback capture from reviewers so that the system can be iteratively improved.

What security risks are specific to agentic AI systems used for code remediation?

Key risks include prompt injection through repository content, insecure or overly broad tool permissions, exposure of sensitive code or credentials, hallucinated fixes, unsafe dependency changes, and bypassing established review controls. I would mitigate these with sandboxing, least-privilege access, trusted context boundaries, policy checks, deterministic testing, logging, and mandatory human approval for sensitive changes.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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