Scribd, Inc.
Senior Machine Learning Engineer - Discovery (ML + Backend Engineering)
Scribd, Inc., Miami, Florida, us, 33222
About The Company
Scribd is on a mission to spark human curiosity. We create a world of stories and knowledge, democratize the exchange of ideas, and empower collective expertise through our products: Everand, Scribd, and Slideshare. We value bold, authentic collaboration and place emphasis on customer success. We offer Scribd Flex, a flexible work benefit that lets employees choose a daily work style in partnership with their manager. Occasional in-person attendance is required for all Scribd employees, regardless of location. We hire for GRIT, defined as the intersection of passion and perseverance toward long-term goals. The acronym GRIT guides our expectations:
G oals,
R esults,
I nnovation, and
T eam collaboration and attitude. The Recommendations Team
The Recommendations team powers personalized discovery across Scribd’s products. We operate at the intersection of large-scale data, ML, and product innovation, collaborating across brands and platforms to enhance reading, listening, and learning experiences. The team comprises frontend, backend, and ML engineers who partner with product managers, data scientists, and analysts. Prototype 0→1 solutions with product and engineering teams. Build and maintain production-grade ML systems for recommendations, search, and generative AI features. Develop services in Go, Python, and Ruby powering high-traffic pipelines. Run large-scale A/B and multivariate experiments to validate models and features. Transform large, diverse datasets into actionable insights with measurable business impact. Explore and implement generative AI for conversational recommendations, document understanding, and advanced search. About The Role
We’re seeking a
Machine Learning Engineer
to design, build, and optimize ML systems that scale to millions of users. You’ll work across the lifecycle—from data ingestion to model training, deployment, and monitoring—to deliver fast, reliable, and cost-efficient pipelines. You’ll contribute to next-generation AI features like doc-chat and ask-AI to expand user interactions with Scribd’s content. Key Responsibilities
Data Pipelines – Collaborate to build large-scale ingestion, transformation, and validation pipelines on Databricks. Model Development & Deployment – Train, evaluate, and deploy ML models to production using internal platforms and standard frameworks. Experimentation – Design and run A/B and N-way experiments to measure impact. Cross-Functional Collaboration – Partner with product managers, data scientists, and analysts to deliver user-focused solutions. Requirements
Must Have 4+ years of post-qualification experience as a professional ML or software engineer with production ML at scale. Proficiency in Python or Go (Scala or Ruby also considered). Experience designing large-scale ML pipelines and distributed systems. Deep experience with distributed data processing (Spark, Databricks, or similar). Strong cloud expertise (AWS, Azure, or GCP) and deployment platforms (ECS, EKS, Lambda). Proven ability to optimize system performance and make informed ML design trade-offs. Experience leading technical projects and mentoring engineers. Bachelor’s or Master’s in Computer Science or equivalent experience. Nice to Have Experience with embedding-based retrieval, large language models, or advanced recommendation systems. Expertise in experimentation design, causal inference, or ML evaluation methodologies. Why Work With Us
High-Impact Environment: Contributions power recommendations, search, and AI features used by millions. Cutting-Edge Projects: Tackle ML/AI problems with a forward-thinking team. Collaborative Culture: A culture that values debate, fresh perspectives, and learning. Flexible Workplace: Scribd Flex with in-person collaboration as a priority. Compensation & Location
Salary ranges are determined by location and level. In the United States, ranges vary by state and market. This position is eligible for competitive equity and a comprehensive benefits package. Salary specifics for California, other US markets, Canada, and market considerations are provided in our job description. Are you based in a location where Scribd can employ you? Primary residence should be in or near eligible cities in the United States, Canada, or Mexico as listed in the job posting. Benefits, Perks, And Wellbeing
Healthcare coverage (Medical/Dental/Vision): 100% paid for employees 12 weeks paid parental leave; disability plans 401k/RSP matching; onboarding stipend for home office setup Learning & Development allowance and programs Wellness, WiFi, and other stipends; mental health resources Free Scribd product subscriptions; referral bonuses; book benefit; sabbaticals Company-wide events, team budgets; vacation, personal days, holidays Volunteer day and inclusive workplace programs Access to AI tools for productivity and innovation Want to learn more about life at Scribd?
www.linkedin.com/company/scribd/life We are committed to equal employment opportunity and encourage applicants from diverse backgrounds. For interview accommodations, email accommodations@scribd.com.
#J-18808-Ljbffr
Scribd is on a mission to spark human curiosity. We create a world of stories and knowledge, democratize the exchange of ideas, and empower collective expertise through our products: Everand, Scribd, and Slideshare. We value bold, authentic collaboration and place emphasis on customer success. We offer Scribd Flex, a flexible work benefit that lets employees choose a daily work style in partnership with their manager. Occasional in-person attendance is required for all Scribd employees, regardless of location. We hire for GRIT, defined as the intersection of passion and perseverance toward long-term goals. The acronym GRIT guides our expectations:
G oals,
R esults,
I nnovation, and
T eam collaboration and attitude. The Recommendations Team
The Recommendations team powers personalized discovery across Scribd’s products. We operate at the intersection of large-scale data, ML, and product innovation, collaborating across brands and platforms to enhance reading, listening, and learning experiences. The team comprises frontend, backend, and ML engineers who partner with product managers, data scientists, and analysts. Prototype 0→1 solutions with product and engineering teams. Build and maintain production-grade ML systems for recommendations, search, and generative AI features. Develop services in Go, Python, and Ruby powering high-traffic pipelines. Run large-scale A/B and multivariate experiments to validate models and features. Transform large, diverse datasets into actionable insights with measurable business impact. Explore and implement generative AI for conversational recommendations, document understanding, and advanced search. About The Role
We’re seeking a
Machine Learning Engineer
to design, build, and optimize ML systems that scale to millions of users. You’ll work across the lifecycle—from data ingestion to model training, deployment, and monitoring—to deliver fast, reliable, and cost-efficient pipelines. You’ll contribute to next-generation AI features like doc-chat and ask-AI to expand user interactions with Scribd’s content. Key Responsibilities
Data Pipelines – Collaborate to build large-scale ingestion, transformation, and validation pipelines on Databricks. Model Development & Deployment – Train, evaluate, and deploy ML models to production using internal platforms and standard frameworks. Experimentation – Design and run A/B and N-way experiments to measure impact. Cross-Functional Collaboration – Partner with product managers, data scientists, and analysts to deliver user-focused solutions. Requirements
Must Have 4+ years of post-qualification experience as a professional ML or software engineer with production ML at scale. Proficiency in Python or Go (Scala or Ruby also considered). Experience designing large-scale ML pipelines and distributed systems. Deep experience with distributed data processing (Spark, Databricks, or similar). Strong cloud expertise (AWS, Azure, or GCP) and deployment platforms (ECS, EKS, Lambda). Proven ability to optimize system performance and make informed ML design trade-offs. Experience leading technical projects and mentoring engineers. Bachelor’s or Master’s in Computer Science or equivalent experience. Nice to Have Experience with embedding-based retrieval, large language models, or advanced recommendation systems. Expertise in experimentation design, causal inference, or ML evaluation methodologies. Why Work With Us
High-Impact Environment: Contributions power recommendations, search, and AI features used by millions. Cutting-Edge Projects: Tackle ML/AI problems with a forward-thinking team. Collaborative Culture: A culture that values debate, fresh perspectives, and learning. Flexible Workplace: Scribd Flex with in-person collaboration as a priority. Compensation & Location
Salary ranges are determined by location and level. In the United States, ranges vary by state and market. This position is eligible for competitive equity and a comprehensive benefits package. Salary specifics for California, other US markets, Canada, and market considerations are provided in our job description. Are you based in a location where Scribd can employ you? Primary residence should be in or near eligible cities in the United States, Canada, or Mexico as listed in the job posting. Benefits, Perks, And Wellbeing
Healthcare coverage (Medical/Dental/Vision): 100% paid for employees 12 weeks paid parental leave; disability plans 401k/RSP matching; onboarding stipend for home office setup Learning & Development allowance and programs Wellness, WiFi, and other stipends; mental health resources Free Scribd product subscriptions; referral bonuses; book benefit; sabbaticals Company-wide events, team budgets; vacation, personal days, holidays Volunteer day and inclusive workplace programs Access to AI tools for productivity and innovation Want to learn more about life at Scribd?
www.linkedin.com/company/scribd/life We are committed to equal employment opportunity and encourage applicants from diverse backgrounds. For interview accommodations, email accommodations@scribd.com.
#J-18808-Ljbffr