- Company Name
- Tubi
- Job Title
- Principal Machine Learning Engineer
- Job Description
-
Job title: Principal Machine Learning Engineer
Role Summary: Lead the design, development, and deployment of scalable recommendation and personalization systems for a global streaming platform. Drive end‑to‑end machine learning pipelines, from data extraction and feature engineering to model training, evaluation, and production integration. Collaborate with product, engineering, and content teams to translate business objectives into robust, high‑performance ML solutions that enhance user engagement.
Expactations:
- Architect and deliver advanced recommendation models that serve millions of users across regions.
- Own the full ML lifecycle, ensuring models are accurate, efficient, and maintainable at scale.
- Mentor and guide cross‑functional teams in best practices for ML engineering, experimentation, and model monitoring.
- Balance deep technical focus with a holistic view of the system architecture and business impact.
Key Responsibilities:
- Design and implement recommendation and content‑understanding algorithms using state‑of‑the‑art deep learning frameworks.
- Build, automate, and maintain production‑ready ML pipelines (data ingestion, feature stores, training workflows, testing, deployment).
- Perform rigorous model validation: hypothesis testing, A/B experiments, performance metrics against business KPIs.
- Continuously monitor deployed models, identify drift, and orchestrate iterative improvement cycles.
- Collaborate with product, engineering, and content stakeholders to define requirements, set expectations, and deliver solutions that drive user engagement.
- Evaluate and adopt new ML tools, libraries, and infrastructure to enhance scalability and efficiency.
Required Skills:
- Expert in deep learning for recommendation systems (TensorFlow, PyTorch, or equivalent).
- Proven experience designing large‑scale, high‑throughput ML pipelines (data extraction, feature engineering, training, testing, deployment).
- Strong statistical knowledge: hypothesis testing, regression, evaluation metrics.
- Expertise in system design, performance tuning, and cloud‑scale deployment (Kubernetes, Docker, scalable storage).
- Ability to decompose complex problems, understand both component details and overall architecture, and communicate solutions to technical and non‑technical audiences.
- Leadership in mentoring, code review, and enforcing engineering standards.
Required Education & Certifications:
- MSc or Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, or a related quantitative field.
- 10+ years of industry experience in machine learning, with demonstrated impact on large‑scale recommendation or personalization systems.