Senior Machine Learning Engineer - Discovery (ML + Backend Engineering)Scribd • United States
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Senior Machine Learning Engineer - Discovery (ML + Backend Engineering)
Scribd
- United States
- United States
About
Senior Machine Learning Engineer - Discovery (ML + Backend Engineering)
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 products: Everand, Scribd, and Slideshare. We support a culture where our employees can be real and 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. Scribd Flex allows employees to choose the daily work-style that best fits their needs, with occasional in-person attendance required to foster collaboration, culture, and connection. We hire for GRIT Go-Goals, Results, Innovation, and Team collaboration. We expect team members to set and achieve goals, deliver results, contribute innovative ideas, and positively influence the broader team through collaboration and attitude. About The Recommendations Team
The Recommendations team powers personalized discovery across Scribds products, delivering relevant and engaging suggestions to millions of users. We operate at the intersection of large-scale data, machine learning, and product innovation, collaborating across brands and platforms to enhance user experiences in reading, listening, and learning. Our team combines Frontend, Backend, and ML engineers who partner with product managers, data scientists, and analysts. Prototype 0?1 solutions in collaboration with product and engineering teams. Build and maintain end-to-end, production-grade ML systems for recommendations, search, and generative AI features. Develop and operate services in Go, Python, and Ruby that power high-traffic recommendation and personalization pipelines. Run large-scale A/B and multivariate experiments to validate models and feature improvements. Transform Scribds massive, diverse dataset into actionable insights that drive measurable business impact. Explore and implement generative AI for conversational recommendations, document understanding, and advanced search capabilities.
About The Role
Were looking for a Machine Learning Engineer who will design, build, and optimize ML systems that scale to millions of users. Youll work across the entire lifecyclefrom data ingestion to model training, deployment, and monitoringwith a focus on fast, reliable, and cost-efficient pipelines. Youll also contribute to next-generation AI features like doc-chat and ask-AI that expand how users interact with Scribds content. Key Responsibilities Data Pipelines Collaborate with engineering and analytics teams to build large-scale ingestion, transformation, and validation pipelines on Databricks. Model Development & Deployment Train, evaluate, and deploy ML models (including generative models) to production using Scribds internal platform and industry-standard frameworks. Experimentation Design and run A/B and N-way experiments to measure the impact of model and feature changes. Cross-Functional Collaboration Partner with product managers, data scientists, and analysts to identify opportunities, define requirements, and deliver solutions that solve real user problems.
Requirements Must Have 4+ years of post-qualification experience as a professional ML 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 (AWS, Azure, or GCP) and experience with deployment platforms (ECS, EKS, Lambda). Proven ability to optimize system performance and make informed trade-offs in ML model and system design. Experience leading technical projects and mentoring engineers. Bachelors or Masters degree in Computer Science or equivalent professional experience.
Nice to Have
Experience with embedding-based retrieval, large language models, advanced recommendation or ranking systems; expertise in experimentation design, causal inference, or ML evaluation methodologies.
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 + accessories Learning & Development allowance and programs Quarterly stipend for Wellness, WiFi, etc. Mental Health support & resources Free subscription to the Scribd Inc. suite of products Referral Bonuses Book Benefit Sabbaticals Company-wide events and team engagement budgets Vacation & Personal Days, Paid Holidays (+ winter break) Flexible Sick Time and Volunteer Day Inclusive workplace with Employee Resource Groups Access to AI Tools to boost productivity
Are you currently based in a location where Scribd is able to employ you? Employees must have their primary residence in or near certain cities in the United States, Canada, or Mexico, including surrounding metro areas. Want to learn more about life at Scribd? www.linkedin.com/company/scribd/life We are 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 diversity of perspectives drives the best ideas. Seniority level
Mid-Senior level Employment type
Full-time Job function
Engineering and Information Technology Industries
Software Development Referrals increase your chances of interviewing at Scribd, Inc. by 2x. Get notified about new Machine Learning Engineer jobs in Houston, TX. #J-18808-Ljbffr
Languages
- English
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