Senior Machine Learning Engineer

Senior Machine Learning Engineer (Search) Scribd, Inc. Join to apply for the Senior Machine Learning Engineer (Search) role at Scribd, Inc. About the Company At Scribd (pronounced “scribbed”), our mission is to spark human curiosity. Join our team as we create a world of stories and knowledge, democratize the exchange of ideas and information, and empower collective expertise through our four products: Everand, Scribd, Slideshare, and Fable. We support a culture where our employees can be real and be bold; where we debate and commit as we embrace plot twists; and where every employee is empowered to take action as we prioritize the customer. When it comes to workplace structure, we believe in balancing individual flexibility and community connections. It’s through our flexible work benefit, Scribd Flex, that employees – in partnership with their manager – can choose the daily work‑style that best suits their individual needs. A key tenet of Scribd Flex is our prioritization of intentional in‑person moments to build collaboration, culture, and connection. For this reason, occasional in‑person attendance is required for all Scribd employees, regardless of their location. We hire for “GRIT” – the intersection of passion and perseverance toward long‑term goals. We’re looking for someone who showcases the ability to set and achieve G oals, R esults, I nnovative ideas, and T eam collaboration. About the Team The Search team powers personalized discovery across Scribd’s products, delivering relevant and engaging suggestions to millions of users. We operate at the intersection of large‑scale data, cutting‑edge machine learning, and product innovation—collaborating across brands and platforms to enhance user experiences in reading, listening, and learning. Our team is a blend of frontend, backend, and ML engineers who partner closely with product managers, data scientists, and analysts. About the Role We’re looking for a Senior Machine Learning Engineer to lead the design, architecture, and optimization of high‑impact ML discovery features that serve millions of users in near real time. You’ll work across the entire lifecycle—from data ingestion to model training, deployment, and monitoring—with a focus on creating fast, reliable, and cost‑efficient pipelines. Lead complex, cross‑team projects from conception to production deployment. Drive technical direction for end‑to‑end, production‑grade ML systems for advanced search capabilities and document understanding. Develop and operate services that power high‑traffic pipelines for content discovery and knowledge synthesis. Run large‑scale A/B and multivariate experiments to validate models and feature improvements. Mentor other engineers and establish best practices for building scalable, reliable ML systems. Tech Stack Languages: Python, Golang, Scala, Ruby on Rails Orchestration & Pipelines: Airflow, Databricks, Spark ML & AI: AWS Sagemaker, Embedding‑based Retrieval (Weaviate), Feature Store, Model Registry, Model Serving platforms (Weights and Biases), LLM providers like OpenAI, Anthropic, Gemini, etc. APIs & Integration: HTTP APIs, gRPC Infrastructure & Cloud: AWS (Lambda, ECS, EKS, SQS, ElastiCache, CloudWatch), Datadog, Terraform Key Responsibilities Train, evaluate, and deploy ML models (including generative models) to production using Scribd’s internal platform and industry‑standard frameworks. Collaborate with engineering and analytics teams to build large‑scale ingestion, transformation, and validation pipelines on Databricks. Optimize systems for performance, scalability, and reliability across massive datasets and high‑throughput services. Design and run A/B and N‑way experiments to measure the impact of model and feature changes. Partner with product managers, data scientists, and analysts to identify opportunities, define requirements, and deliver solutions that solve real user problems. Requirements 6+ years of experience as a professional ML engineer or software engineer, with a proven track record of delivering production ML systems at scale. Proficiency in at least one key programming language (preferably Python or Golang; Scala or Ruby also considered). Expertise in designing and architecting large‑scale ML pipelines and distributed systems. Deep experience with distributed data processing frameworks (Spark, Databricks, or similar). Strong cloud expertise (preferably GCP; also AWS and/or Azure) and experience with deployment platforms (ECS, EKS, Lambda). Experience with embedding‑based retrieval, large language models, advanced information retrieval and ranking systems. Experience working with Search systems like query parsing, query intent classification, bm25, reranking, etc. Proven ability to optimize system performance and make informed trade‑offs in ML model and system design. Experience leading technical projects and mentoring engineers. Bachelor’s or Master’s degree in Computer Science or equivalent professional experience. Compensation and Benefits Base pay is part of a total compensation package determined within a range. Salary ranges: San Francisco, CA: $157,500–$230,000. California, other U.S.: $129,500–$220,000. Canada: $165,000 CAD–$218,000 CAD. Position also eligible for equity ownership and a comprehensive benefits package. Working at Scribd, Inc. Are you currently based in a location where Scribd is able to employ you? Employees must have their primary residence in or near one of the following cities (USA, Canada, Mexico): United States: Atlanta, Austin, Boston, Dallas, Denver, Chicago, Houston, Jacksonville, Los Angeles, Miami, New York City, Phoenix, Portland, Sacramento, Salt Lake City, San Diego, San Francisco, Seattle, Washington D.C. Canada: Ottawa, Toronto, Vancouver. Mexico: Mexico City. Benefits, Perks, and Wellbeing at Scribd Healthcare Insurance Coverage (Medical/Dental/Vision): 100% paid for employees 12 weeks paid parental leave Short‑term/long‑term disability plans 401k/RSP matching Onboarding stipend for home office peripherals Learning & Development allowance and programs Quarterly stipend for Wellness, WiFi, etc. Mental Health support & resources Free subscription to the Scribd suite of products Referral Bonuses Book Benefit Sabbaticals Company‑wide events Team engagement budgets Vacation & Personal Days Paid Holidays (+ winter break) Flexible Sick Time Volunteer Day Company‑wide Employee Resource Groups and programs fostering inclusivity and diversity. Access to AI Tools: free access to best‑in‑class AI tools to boost productivity, streamline workflows, and accelerate bold innovation. Accommodations We want our interview process to be accessible to everyone. You can inform us of any reasonable adjustments we can make to better accommodate your needs by emailing at any point in the interview process. Equal Employment Opportunity Statement Scribd is committed to equal employment opportunity regardless of race, color, religion, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law. We encourage people of all backgrounds to apply, and believe that a diversity of perspectives and experiences create a foundation for the best ideas. Come join us in building something meaningful. #J-18808-Ljbffr