The Company

Pocket empowers people to discover, organize, consume, and share content that matters to them. Our apps and platform are essential ways that tens of millions of people discover and consume content on the web. Pocket is the Web, curated: for you and by you.

The Opportunity

Build the systems connecting millions of people to the content worthy of their time and attention. We are looking for a Staff Machine Learning engineer to partner with the editorial and product teams to develop the recommendation systems that power Pocket’s discovery surfaces and the Firefox new tab. Machine learning engineers are responsible for designing and evolving both algorithmic and human-in-the-loop recommendation systems. Pocket is committed to recommending high-quality content at scale while respecting our readers' data and privacy.

We seek applied machine learning experts who can both design algorithms and architectures to satisfy product requirements and also help deploy their solutions in production. Pocket recommendations combine content and engagement signals with the human expertise of our editorial team. Our recommendation systems reflect editorial principles, Mozilla's values, and the pocket mission. We optimize our systems to enable users to discover content that they can consume on their terms and at their pace.

People who excel on our team thrive in small, dynamic environments. We contribute in many areas beyond machine learning, including product engineering, machine learning operations, and data modeling, among others.

What you'll do

  • Work closely with Pocket editorial, product management, and experience design teams to deliver high-quality features and solutions

  • Be involved in end-to-end development, exploring new applications and techniques within natural language processing, applied machine learning, and explainable and privacy-aware ML

  • Enhance our machine learning infrastructure.

  • Write robust production-level code and engage in code reviews.

What you bring

  • Technical education in Computer Science, Information Science, Engineering, or equivalent experience (Master's degree or higher preferred)

  • 3+ years of relevant experience in Machine Learning Engineering:

    • shipping high-quality machine learning solutions to production

    • design and analysis of experiments (A/B tests) to validate iterative system development

    • conceptual familiarity with feature stores, monitoring and observability, data distribution shifts

  • Flexible and comfortable working within a distributed organization and with minimal process. This role allows for a high degree of ownership and requires rapid iteration.

  • Motivation to learn continuously and help define research and development practices in an applied setting

  • Excellent verbal and written communication skills and willingness to convey algorithmic or engineering considerations to non-experts.

  • Experienced in building recommendation systems on cloud platforms like AWS Sagemaker or Google Cloud ML.

Bonus experience

  • AWS SageMaker, Google Cloud ML

  • ML: Scikit-Learn, PyTorch, Hugging Face, Metaflow

  • Storage: Snowflake, AWS Feature Groups, AWS S3, AWS DynamoDB

  • Orchestrators: Airflow, Prefect

  • Data modeling: DBT

Group: C

#LI-remote

R1865

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