Scribd, Inc.
Senior Machine Learning Engineer - Discovery (ML + Backend Engineering)
Scribd, Inc., San Francisco, California, United States, 94199
About The Company
At Scribd Inc., our mission is to spark human curiosity. 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 employees can be real and bold, debate, commit, and embrace plot twists. Every employee is empowered to take action as we prioritize the customer. Workplace structure balances individual flexibility with community connections. Scribd Flex allows employees to choose a daily work style that best suits their needs, while prioritizing intentional in‑person moments. Occasional in‑person attendance is required for all employees, regardless of location. We value
GRIT —the intersection of passion and perseverance towards long‑term goals. We look for people who can set and achieve
G oals,
R esults,
I nnovation, and
T eam influence. About The Recommendations Team
The Recommendations 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. 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 Scribd’s 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
We’re looking for a
Machine Learning Engineer
who will design, build, and optimize ML systems that scale to millions of users. You’ll work across the entire lifecycle—from data ingestion to model training, deployment, and monitoring—focusing on creating fast, reliable, and cost‑efficient pipelines. 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 Scribd’s 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. Bachelor’s or Master’s 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. Why Work With Us
High‑Impact Environment: Your contributions will power recommendations, search, and next‑generation AI features used by millions of readers, learners, and listeners worldwide. Cutting‑Edge Projects: Tackle challenging ML and AI problems with a forward‑thinking team, building novel generative features on top of Scribd’s massive and unique dataset. Collaborative Culture: Join a culture that values debate, fresh perspectives, and a willingness to learn from each other. Flexible Workplace: Benefit from Scribd Flex, which offers autonomy in choosing your daily work style, while still prioritizing in‑person collaboration. Benefits, Perks, And Wellbeing At Scribd Inc.
Healthcare insurance coverage (Medical/Dental/Vision): 100% paid for employees. 12 weeks paid parental leave. Short‑term/long‑term disability plans. 401(k)/RSP matching. Onboarding stipend for home office peripherals and accessories. Learning & development allowance and programs. Quarterly stipend for wellness, Wi‑Fi, etc. Mental health support & resources. Free subscription to the Scribd Inc. suite of products. Referral bonuses. Book benefit. Sabbaticals. Company‑wide events. Team engagement budgets. Vacation & personal days. Paid holidays (plus winter break). Flexible sick time. Volunteer day. Company‑wide employee resource groups and programs that foster an inclusive and diverse workplace. Access to AI tools: free access to best‑in‑class AI tools to boost productivity, streamline workflows, and accelerate innovation. Want to learn more about life at Scribd?
Visit
www.linkedin.com/company/scribd/life . 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 accommodations@scribd.com at any point in the interview process. Scribd Inc. 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
At Scribd Inc., our mission is to spark human curiosity. 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 employees can be real and bold, debate, commit, and embrace plot twists. Every employee is empowered to take action as we prioritize the customer. Workplace structure balances individual flexibility with community connections. Scribd Flex allows employees to choose a daily work style that best suits their needs, while prioritizing intentional in‑person moments. Occasional in‑person attendance is required for all employees, regardless of location. We value
GRIT —the intersection of passion and perseverance towards long‑term goals. We look for people who can set and achieve
G oals,
R esults,
I nnovation, and
T eam influence. About The Recommendations Team
The Recommendations 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. 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 Scribd’s 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
We’re looking for a
Machine Learning Engineer
who will design, build, and optimize ML systems that scale to millions of users. You’ll work across the entire lifecycle—from data ingestion to model training, deployment, and monitoring—focusing on creating fast, reliable, and cost‑efficient pipelines. 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 Scribd’s 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. Bachelor’s or Master’s 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. Why Work With Us
High‑Impact Environment: Your contributions will power recommendations, search, and next‑generation AI features used by millions of readers, learners, and listeners worldwide. Cutting‑Edge Projects: Tackle challenging ML and AI problems with a forward‑thinking team, building novel generative features on top of Scribd’s massive and unique dataset. Collaborative Culture: Join a culture that values debate, fresh perspectives, and a willingness to learn from each other. Flexible Workplace: Benefit from Scribd Flex, which offers autonomy in choosing your daily work style, while still prioritizing in‑person collaboration. Benefits, Perks, And Wellbeing At Scribd Inc.
Healthcare insurance coverage (Medical/Dental/Vision): 100% paid for employees. 12 weeks paid parental leave. Short‑term/long‑term disability plans. 401(k)/RSP matching. Onboarding stipend for home office peripherals and accessories. Learning & development allowance and programs. Quarterly stipend for wellness, Wi‑Fi, etc. Mental health support & resources. Free subscription to the Scribd Inc. suite of products. Referral bonuses. Book benefit. Sabbaticals. Company‑wide events. Team engagement budgets. Vacation & personal days. Paid holidays (plus winter break). Flexible sick time. Volunteer day. Company‑wide employee resource groups and programs that foster an inclusive and diverse workplace. Access to AI tools: free access to best‑in‑class AI tools to boost productivity, streamline workflows, and accelerate innovation. Want to learn more about life at Scribd?
Visit
www.linkedin.com/company/scribd/life . 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 accommodations@scribd.com at any point in the interview process. Scribd Inc. 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