Senior Machine Learning Engineer - Applied Research

Full Time

At Scribd (pronounced “scribbed”), we believe reading is more important than ever. Join our cast of characters as we build the world’s largest and most fascinating digital library: giving subscribers access to a growing collection of ebooks, audiobooks, magazines, documents, Scribd Originals and more. In addition to works from major publishers and top authors, our community includes over 1.4M subscribers in nearly every country worldwide.

The Applied Research team at Scribd has a growing demand for infrastructure-related tasks, which highlights the necessity to bring Machine Learning Engineers (MLE) into the team. Although we aim to start with a single MLE, we identify long term projects and responsibilities that would eventually require the attention of multiple individuals.
We seek a seasoned individual to own projects in the adjacency of building a ML model pipeline and developing a more effective productionization process for ML models. 

-ML systems design and implementation.
- Defining interfaces, data infrastructure and hardware to meet project requirements in collaboration with participating teams.
- Designing and implementing ML pipelines to process data efficiently.
- Collaborate with the teams working on the ML platform to support logging, monitoring, scalability, model retraining, automated testing, environment isolation, health and decay monitoring.
- Deploying and monitoring ML models in production environments, in collaboration with Data Scientists.
Work in a highly collaborative environment to solve ambiguous problems.
Collaborate with stakeholders to determine the highest-impact opportunities.

Required Skills:
Python, Spark and SQL.
- Frameworks and environments such as Airflow, Docker.
- Fundamental ML and stats concepts and exposure ML libraries.

- 3 years or more of relevant work experience with building and automating ML pipelines and deploying ML models in production environments.
- Knowledge of relevant software design patterns and system architecture.
- Experience with scalable software using big data technologies (e.g. Spark).Strong communication skills and ability to collaborate with multiple teams.

Nice to have:
- Experience with machine learning and deep learning frameworks such as TensorFlow, Pytorch or Scikit-learn.
- Experience using Databricks and/or ML Deployment and Operations platforms and tools
Benefits, Perks and Wellbeing at Scribd

• Healthcare Benefits: Scribd pays 100% of employee’s Medical, Vision, and Dental premiums and 70% of dependents
• Leaves: Paid parental leave, 100% company paid short-term/long-term disability plans, and milestone Sabbaticals
• 401k plan through Fidelity,  plus company matching with no vesting period
• Diversity, Equity, & Inclusion hiring best practices
• Stock Options - every employee is an owner in Scribd! 
• Generous Paid Time Off, Paid Holidays, Flexible Sick Time, Volunteer Day + office closure between Christmas Eve and New Years Day
• Referral bonuses
• Professional development: generous annual budget for our employees to attend conferences, classes, and other events
• Company-wide Diversity, Equity & Inclusion training
• Learning & Development and Coaching programs
• Monthly Wellness, Connectivity & Comfort Benefit
• Concern mental health digital platform
• Work-life balance flexibility
• Employee Resource Groups that build community and support among employees
• Company events + Scribdchats
• Free subscription to Scribd + gift memberships for friends & family
• Monthly inclusive multi-cultural celebrations & learning opportunities

Want to learn more? Check out our office and meet some of the team at

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. We believe that a diversity of perspectives and experiences create a foundation for the best ideas. Come join us in building something meaningful.
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