Note: The job is a remote job and is open to candidates in USA. Penguin Random House is the largest trade publishing company in the world, and they are seeking a Staff Machine Learning Scientist to lead the development of personalization products. The role involves owning the end-to-end process of personalization and recommender systems, collaborating with various stakeholders to enhance customer engagement and book discovery.
Responsibilities
- Define and drive the technical roadmap for personalization and recommender systems, prioritizing roadmap items to meet business goals and defining short-term vision for the team
- Propose and deliver R&D that directly shapes roadmaps, multiple projects, and long-term deliverables. Models are used over the long term by multiple products and teams
- Design and lead the development of software used by multiple teams, ensuring long-term maintainability, scalability, and adaptability
- Ensure complex, multi-service personalization products meet SLAs and provide correct results over time. Adapt systems to changing business needs and resolve multi-product, multi-team service incidents
- Establish and enforce experimentation best practices, including A/B testing frameworks, offline evaluation methodology, and metrics design across personalization surfaces
- Lead team meetings, ensure the team's progress on the roadmap, and make technical decisions that unblock projects
- Manage stakeholders' expectations with data-driven narratives and communicate effectively with senior leadership to align on strategy and track progress
- Drive organizational efficiency and business impact by implementing new technologies and processes. Foster a collaborative and high-performance team culture
- Mentor senior and mid-level scientists, setting high code quality standards and best practices for the team
- Stay current with advances in recommender systems, LLMs for personalization, and representation learning, bringing relevant advances into production when they deliver measurable improvement
Skills
- PhD in Computer Science, Machine Learning, Engineering, Operations Research, Statistics, or a related quantitative field, OR Master's with 8+ years of applied ML experience
- Deep expertise in recommender systems, personalization, ranking/retrieval, or computational advertising, with a track record of shipping systems that operate at scale
- Expert-level Python and deep proficiency with modern ML frameworks (PyTorch or TensorFlow) and recommendation-specific tooling (e.g., NVTabular, Merlin, Triton)
- Strong experience with cloud-based ML infrastructure (AWS, Kubernetes, Databricks), containerization (Docker), and model serving at low latency
- Advanced SQL skills and experience architecting large-scale data pipelines and feature stores
- Demonstrated ability to define technical roadmaps, influence direction across teams, and make architectural decisions that hold up over time
- Excellent communication skills with the ability to present complex technical work to executive and non-technical audiences
- Be cutting edge. Use the latest AI tools to develop well-designed and robust software
- Experience building and scaling real-time recommendation services handling millions of requests
- Expertise in A/B testing methodology, causal inference, or experimentation platforms
- Familiarity with LLM-based approaches to recommendation and content understanding
- Experience with MLOps practices: model monitoring, feature stores, CI/CD for ML, and automated retraining pipelines
- Prior experience technically leading a team of ML practitioners and setting standards adopted by others
Benefits
- Annual profit award or bonus, subject to company results
- Medical/Prescription drug insurance
- Dental
- Vision
- Health Care/Dependent Care Flexible Spending Account
- Health Savings Account
- Pre-Tax and Roth 401(k)
- Short and Long-Term Disability Insurance
- Life/AD&D Insurance
- Commuter Benefits
- Student Loan Repayment Program
- Educational Assistance
- Generous paid time off
Company Overview