We support a culture where our employees can be real and be bold; and where every employee is empowered to take action as we prioritize the customer. We believe the best work happens when individual flexibility is balanced with meaningful community connection.

Scribd

Flex empowers employees to choose the workstyle and location that support their best performance, while committing to intentional in‑person moments that strengthen collaboration and culture. Traditionally defined as the intersection of passion and perseverance toward long‑term goals, GRIT reflects the mindset we expect from every employee. For us, it also serves as a practical framework for how we work: setting and achieving Goals, delivering Results within your role, contributing Innovative ideas and solutions, and strengthening the broader Team through collaboration and attitude.

Everand, the audiobook and ebook subscription app, and Fable, the social app where readers track, discuss and discover books. Helping readers find their next book is central to both, so search and recommendations sit at the heart of the products. It's a small, dedicated group of recommendations, search, applied research and ML engineers working full time on discovery for Everand and Fable.

The squad works closely with Orion, Scribd’s ML platform team. Orion runs the feature store, model registry, embedding‑based retrieval and model serving behind discovery across Scribd, Slideshare and Everand.

Role Overview We’re looking for a Senior Machine Learning Engineer to own recommendations for Fable and grow into the technical lead for Premium recommendations. Fable’s recommender today is a collaborative‑filtering model built in house. It has served readers well, but it has no real‑time signals and no learned ranking, and it can’t recommend to new or lapsed readers.

Your first big project is to move Fable onto Orion, so Fable and Everand share a modern recommendation stack. Lead the migration of Fable’s recommendations onto Orion, covering data, features, training, serving and experiments, while keeping today’s recommendations running.

Extend

Orion’s rankers,



feature store and embedding‑based retrieval to support Fable, in partnership with the Orion ML platform team. Own the health of Fable’s recommendation systems in production: quality, latency, cost and on‑call.

Bring

Fable’s social signals (shelves, ratings, reviews, book clubs, reader similarity) into recommendations for both Fable and Everand. Design and analyze A/B tests with product managers and analysts to decide what ships. Act as technical lead for Premium recommendations. Raise the bar on engineering practice in the squad through design reviews, code quality and operational readiness. Fable’s on Google Cloud and Orion’s on AWS.

Fable: Python (FastAPI, Django), PostgreSQL, BigQuery, Kubernetes, Google Cloud including Vertex AI Python, Go and gRPC, Ruby on Rails, Databricks and Spark, Airflow, AWS SageMaker, Weaviate for embedding‑based retrieval, feature store and model registry Infrastructure: AWS (EKS, ECS, Lambda, SQS, ElastiCache), Datadog, Terraform 5+ years as a professional ML or software engineer, with a track record of shipping production ML systems. ~ Hands‑on experience building and running recommender systems in production, such as collaborative filtering or matrix factorization, two‑tower or embedding retrieval, and learned ranking. ~ Experience with large‑scale data processing (Spark, Databricks, BigQuery or similar). ~ Experience on at least one major cloud (AWS or GCP), including deploying services on containers (Kubernetes, ECS or similar). ~ Experience designing and analyzing online experiments. ~ A record of leading projects that depend on other teams, and of working directly with product managers. ~ Bachelor’s or Master’s degree in Computer Science, or equivalent professional experience.

Google





Cloud experience, especially Vertex AI and BigQuery.

Experience migrating or replacing a production ML system while keeping it running. Social or graph‑based recommendations, such as user similarity, follows or groups. Search and semantic retrieval (Elasticsearch, Vertex AI Search, vector databases).

Experience on consumer apps for reading, media or social. Our pay ranges are based on the local cost of labor benchmarks for each specific role, level, and geographic location. In Canada, the reasonably expected salary range is between 179,000 CAD[minimum salary in our lowest geographic market] to 237,500 CAD[maximum salary in our highest geographic market]. relevant education or training; In the event that you are considered for a different level, a higher or lower pay range would apply.

This position is also eligible for a competitive equity ownership, and a comprehensive and generous benefits package.

Scribd

Flex (versatile work model) Comprehensive health, dental, and vision coverage Mental health support and disability coverage Generous paid time off, including vacation, sick time, holidays, winter break, volunteer time, and sabbaticals Paid parental leave and family support benefits Retirement matching and employee equity Wellness and home office stipends Enterprise access to leading AI tools If you apply for a job with Scribd, Inc. or otherwise engage with us in connection with employment (including as an employee, contractor, or other personnel), the personal information we process in that context is subject to our Employee and Applicant Privacy Policy, which is available here. 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. #

📌 Senior Machine Learning Engineer, Premium Discovery (Toronto)
🏢 Scribd
📍 Toronto