[All remote jobs](https://jobicy.com/jobs.md)[![Spotify logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2021/03/Jobicy-210328105941-289246.jpg)](https://jobicy.com/company/spotify.md)[Spotify](https://jobicy.com/company/spotify.md) # Senior Staff Machine Learning Engineer – Content Platform AI Summary Spotify is seeking a Senior Staff Machine Learning Engineer to define and scale foundational ML systems for its global content platform. The role focuses on multimodal content understanding, moderation, classification, risk detection, policy enforcement, and automated decisioning across text, audio, image, and video. The engineer will set technical strategy, design production systems operating in real time, and ensure quality, safety, fairness, explainability, and reliability. This is a senior technical leadership position requiring cross-functional collaboration with Product, Policy, Trust & Safety, and Engineering teams, as well as mentoring and architectural influence. The role is New York-based with work-from-home flexibility and some required in-person meetings.[Apply for this job](#job-application)[View company](https://jobicy.com/company/spotify.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md)SalaryUSD 281,196–401,709 / yrDepartment[Software Engineering](https://jobicy.com/categories/engineering.md)EmploymentFull TimeExperienceSeniorPublished6 Nov 2026Apply before6 Dec 2026Listing views31Application actions1Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. Opportunity details ## About this role. We design Spotify’s consumer experience—end to end, moment to moment, across every screen, platform, and partner integration. Our mission is to make listening feel effortless, personal, and joyful for billions of users around the world. That means turning complexity into clarity across hundreds of touchpoints—from our mobile and desktop apps to the smart speakers, TVs, cars, and integrations where Spotify shows up every day. If it touches a consumer, we shape it. We bring deep insight into human behavior, design, and technology to craft experiences that feel intuitive, expressive, and unmistakably Spotify. The Content Platform team powers the full lifecycle of content across music, podcasts, audiobooks, and emerging formats at Spotify. We ensure that everything from licensed catalog to user-generated content is understood, trusted, safe, and high quality for millions of listeners worldwide. Our systems are responsible for how content is ingested, understood, enriched, governed, and distributed across the platform. As the scale and diversity of content continue to grow—driven by advances in AI, new creation tools, and emerging content formats—we’re investing in intelligent systems that can understand, evaluate, manage, and route content reliably at global scale. We’re hiring Senior Staff Machine Learning Engineers to build and scale foundational ML systems that power content understanding, safety, policy enforcement, and decisioning across Spotify. Depending on your experience and interests, you may focus on areas ranging from multimodal content understanding and platform-level ML systems to safety, risk detection, policy enforcement, and compliance. You’ll help shape the architecture and technical strategy behind how Spotify understands and makes decisions about content at global scale. This work is foundational to delivering safe, high-quality experiences for listeners and creators while enabling new ways for people to interact with content across Spotify. ### What You Will Do * Define and drive machine learning strategy across content understanding, safety, policy enforcement, and platform-level decisioning * Build and scale production ML systems for classification, moderation, ranking, risk detection, and content evaluation * Develop automated decisioning systems that support content quality, integrity, safety, and policy compliance at scale * Design and deploy ML systems across multiple content modalities, including text, audio, image, and video * Build systems capable of evaluating and making decisions about content in real time and at Spotify scale * Enable controlled and reliable access to content and metadata for downstream products and applications * Collaborate closely with Product, Policy, Trust & Safety, and Engineering teams to translate content standards into scalable technical solutions * Advance automation while maintaining high standards for quality, safety, fairness, explainability, and reliability * Provide technical leadership across teams, mentoring engineers and influencing ML engineering, evaluation, and system design best practices ### Who You Are * You have significant experience designing and building production-grade machine learning systems at scale * You have deep experience with modern ML frameworks such as PyTorch, TensorFlow, JAX, or similar * You have experience with—or a strong interest in—multimodal machine learning across areas such as text, audio, image, or video * You have built systems where machine learning outputs inform or automate real-world decisions * You understand how to balance automation with quality, safety, reliability, and user experience * You’re comfortable tackling complex and ambiguous technical problems with significant product and business impact * You think in systems, connecting models, data, infrastructure, and decisioning to platform-level outcomes and user experiences * You care deeply about data quality, rigorous evaluation, fairness, explainability, and system reliability * You’re an experienced technical leader who communicates clearly, mentors others, and can influence across technical and non-technical teams ### Where You Will Be * This role is based in New York. * 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 The United States base range for this position is $281,196 – $401,709 plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, paid sick leave. These ranges may be modified in the future. Show more [Apply now >](https://jobicy.com/jobs/159258-senior-staff-machine-learning-engineer-content-platform.md) * ![Upload CV](data:image/svg+xml;base64,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) ### Upload your resume now To unlock remote work opportunities and be discovered by global employers. 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 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightРабота требует глубокой экспертизы в production ML, мультимодальных моделях и распределённых системах, работающих в глобальном масштабе. Сложность повышают ответственность за безопасность контента, соответствие политикам и влияние на техническую стратегию нескольких команд. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$341,452.50US market range (per year)$280k–$425k0$467.50k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. Работодатель указывает базовый диапазон $281,196–$401,709 USD плюс equity. Годовой период для базовой оплаты выведен логически по масштабу суммы, американскому рынку и уровню Senior Staff, так как в тексте он прямо не указан. Оценочный рыночный диапазон для аналогичной Senior Staff ML Engineering роли в США составляет $280,000–$425,000 USD в год без учёта equity и бонусов. ## Core skills Skills and capabilities most closely associated with this opportunity. [Machine Learning](https://jobicy.com/jobs?search_keywords=Machine%20Learning.md)[ML System Design](https://jobicy.com/jobs?search_keywords=ML%20System%20Design.md)[Multimodal Machine Learning](https://jobicy.com/jobs?search_keywords=Multimodal%20Machine%20Learning.md)[PyTorch](https://jobicy.com/jobs?search_keywords=PyTorch.md)[TensorFlow](https://jobicy.com/jobs?search_keywords=TensorFlow.md)[JAX](https://jobicy.com/jobs?search_keywords=JAX.md)[Content Moderation](https://jobicy.com/jobs?search_keywords=Content%20Moderation.md)[Trust and Safety](https://jobicy.com/jobs?search_keywords=Trust%20and%20Safety.md)[Risk Detection](https://jobicy.com/jobs?search_keywords=Risk%20Detection.md)[Classification Models](https://jobicy.com/jobs?search_keywords=Classification%20Models.md)[Ranking Systems](https://jobicy.com/jobs?search_keywords=Ranking%20Systems.md)[Real-Time Decisioning](https://jobicy.com/jobs?search_keywords=Real-Time%20Decisioning.md)[Model Evaluation](https://jobicy.com/jobs?search_keywords=Model%20Evaluation.md)[Explainable AI](https://jobicy.com/jobs?search_keywords=Explainable%20AI.md)[Technical Leadership](https://jobicy.com/jobs?search_keywords=Technical%20Leadership.md) Sample interview questionsHow would you design a production ML system for multimodal content safety classification at Spotify scale?I would begin by defining policy-aligned labels, risk tiers, latency targets, and escalation paths. The architecture would combine modality-specific feature pipelines with multimodal models, versioned training data, online inference, confidence calibration, and a human-review route for uncertain or high-impact decisions. I would also build continuous monitoring for drift, quality, fairness, latency, and policy-outcome metrics. How do you balance automation with fairness, explainability, and user experience in content enforcement systems? I use risk-based automation thresholds rather than treating every decision equally. High-confidence, low-risk cases can be automated, while ambiguous or high-impact cases receive additional model checks or human review. I would measure disparate error rates across relevant segments, preserve decision traces, and establish appeal and feedback mechanisms that improve both policy outcomes and user trust. Describe how you would evaluate an ML model whose output informs real-world content decisions. I would evaluate offline performance using representative, policy-reviewed datasets and metrics such as precision, recall, calibration, false-positive rates, and segment-level performance. Before broad rollout, I would use shadow deployment or controlled experiments to compare model recommendations with existing decisions. Post-launch, I would monitor live quality, reviewer disagreement, appeals, operational impact, latency, and distribution drift. What technical challenges arise when deploying real-time ML decisioning systems across text, audio, image, and video? The main challenges include heterogeneous preprocessing, variable input size, inference latency, model-serving cost, data-quality differences, and consistent policy application across modalities. I would address these through asynchronous processing where appropriate, optimized serving tiers, reusable feature and evaluation infrastructure, confidence-aware routing, and clear service-level objectives for each decision type. How would you influence technical strategy across engineering, product, and policy stakeholders? I would translate business and policy goals into explicit technical capabilities, measurable success criteria, and phased delivery plans. I would communicate trade-offs clearly, use prototypes and data to make decisions tangible, and create shared mechanisms for reviewing model quality and operational risk. As a staff-level leader, I would also mentor engineers and establish reusable standards that allow multiple teams to execute consistently. Next step ## Apply now. Follow the employer’s application method and review Jobicy’s safety guidance before sharing personal information. 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