Jobs at Scribd, Inc.

Design, implement, and scale event-driven, distributed backend systems in Python on AWS to process and enrich large-scale document and media datasets. Lead technical projects, mentor engineers, integrate ML/LLM components, optimize performance, maintain observability, and collaborate with Data Science, ML Engineering, and Product to deliver reliable high-throughput content processing services.

Design and implement features for content ingestion pipelines, metadata services, and content processing workflows. Build reliable, observable services that handle diverse file formats, malformed inputs, retries, and asynchronous workflows. Collaborate with ML, Search & Discovery, and Product to improve metadata extraction, classification, and discoverability while leveraging LLM-based tools and AI-driven practices.

Design and optimize the end-to-end self-service customer journey across Help Center content, automation, chat and email. Own Help Center strategy and content governance, track KPIs (visits, ratings, SEO/AEO), partner with product/engineering/support teams, manage translations and monthly content tasks, use AI to scale content, and reduce support volume through improved self-service.

Lead zero-to-one product initiatives to transform knowledge discovery using ML/LLMs. Define vision, roadmap, and success metrics; prioritize trade-offs; partner with Design, Engineering, ML, and Data Science; deliver incremental wins while building an extensible platform and improving prospect and subscriber experiences across devices.

Lead and own engineering vision, strategy, and execution for Scribd's growth organization. Build and run growth/MarTech platform, manage ~25 engineers and a 5-person platform team, partner cross-functionally to deliver data pipelines, experimentation, messaging, and attribution systems, and drive adoption of agentic/AI practices while maintaining engineering quality and scaling impact.

Design, build, and deploy ML and AI systems at scale for content classification, recommendations, search, and metadata enrichment. Work with cross-functional teams to process large datasets, develop models from classical NLP to LLMs, implement production pipelines, and communicate results to stakeholders.

Lead design and scaling of content ingestion, metadata, and processing systems. Build reliable pipelines handling messy formats, collaborate with ML, security, and product teams, mentor engineers, and drive platform and AI/LLM integration for content workflows and quality.

Join the Signup & Activation team to build and optimize frontend experiences that drive user acquisition and early activation. Collaborate with product, design, analytics, and backend engineers to implement high-quality, performant React/TypeScript interfaces, run experiments (A/B testing and personalization), and own well-scoped features from design through delivery in a Rails-backed environment.

Lead a 5-person UGC product design team, set strategic priorities, integrate AI into design workflows, improve systems and cross-team handoffs, manage performance and growth, and partner with senior product and research leadership to drive measurable product impact across Scribd and Slideshare.

Lead product analytics for zero-to-one ML/LLM-powered experiences: define measurement frameworks, design and evaluate experiments, build instrumentation and KPI systems, partner with Product/ML/Engineering to drive data-informed product strategy and scalable dashboards.

Lead and scale a small TPM team owning strategy and execution for Scribd's Premium product line. Drive cross-functional programs from roadmap to delivery, manage timelines and risks, define success metrics, improve processes, and mentor direct reports while collaborating closely with product, engineering, and marketing stakeholders.

Lead the product strategy for Scribd's internal developer and agentic platforms: unify AI workstreams, own internal app/plugin/agent runtimes, translate functional needs into platform requirements, drive cross-functional adoption and metrics, and run internal rollouts and evaluation to prove impact.

Design, build, and optimize event-driven, distributed backend systems and APIs in Python for large-scale content metadata extraction and enrichment. Deploy and operate resilient AWS-based pipelines, collaborate with ML teams to integrate models, and ensure system performance, observability, and data integrity.

Own and scale quality infrastructure for web engineering during a transition from a monolith to microfrontends. Build shared testing frameworks, CI/CD quality gates, metrics, and AI-enabled tooling to improve test ownership, reliability, and developer productivity. Mentor engineers, define testing strategy across unit/component/integration/contract layers, and create verification loops for agentic code generation.

Build and optimize backend distributed systems to extract, enrich, and process metadata at scale. Integrate LLMs and ML models into production pipelines, ensure data quality and monitoring, collaborate with ML engineers and product, and maintain infrastructure, security, and performance of metadata systems.

Lead and scale paid social advertising across Meta, TikTok, Reddit, and emerging platforms to drive user acquisition for subscription products. Own budgets, audience targeting, structured testing, creative collaboration, AI-accelerated experimentation, analytics, attribution and incrementality work, and conversion rate optimization across landing pages. Communicate results and build reusable playbooks for the performance marketing team.

Lead technical direction and architecture for the Fable backend, own platform reliability (databases, CI/CD, queues, observability), run incident response and postmortems, mentor engineers, collaborate cross-functionally with product/design/ML, and build platform capabilities enabling faster, safer delivery.

Design, build, and scale event-driven, distributed backend systems in Python to extract and enrich metadata at massive scale. Lead architecture, mentor engineers, integrate ML/LLM components, optimize performance, and ensure observability and reliability across AWS-based pipelines.

Build and optimize frontend signup and activation experiences using modern JavaScript and TypeScript. Collaborate with product, design, and analytics to implement high-converting, accessible, and performant user flows, own features from design through implementation, and participate in experimentation and personalization to improve conversion.

Lead AI engineering on the Data Platform: build Databricks Apps deployment paths, construct an AI layer over the Medallion Architecture and semantic layer, enable LLM/NLP systems and agentic workflows, embed AI tooling in the developer lifecycle, enforce guardrails for production safety, define metadata/modeling patterns, and mentor engineers to accelerate AI adoption.