Senior Machine Learning Engineer, Recommendation
New
Based in the United StatesFull-TimeSenior
Salary not disclosed
Apply NowOpens the employer's application page
Job Details
- Experience
- 5+ years
- Required Skills
- Machine LearningA/B testingDistributed Systems
Requirements
- 5+ years of industry experience building and deploying production machine learning systems with significant ownership and technical leadership responsibilities.
- Proven experience developing recommendation engines, search systems, ranking models, feed optimization systems, advertising ranking platforms, or content discovery solutions.
- Strong background working on consumer-facing applications, particularly within social platforms, entertainment products, gaming ecosystems, creator platforms, or engagement-driven experiences.
- Hands-on expertise with retrieval and ranking architectures, including embedding-based retrieval, candidate generation, two-tower models, feature engineering, and relevance optimization.
- Strong understanding of online and offline evaluation methodologies, experimentation frameworks, and machine learning performance measurement.
- Excellent product intuition with a deep understanding of relevance, engagement, retention, satisfaction, personalization, and content distribution dynamics.
- Experience transforming large-scale behavioral data into meaningful machine learning signals and business outcomes.
- Solid engineering fundamentals, including data pipelines, backend integrations, distributed systems, production ML deployment, and model lifecycle management.
- Strong communication and collaboration skills with the ability to work effectively across technical and non-technical teams.
- High degree of ownership, adaptability, and problem-solving ability in fast-paced and ambiguous environments.
Responsibilities
- Design, build, and continuously improve recommendation and search systems across content feeds, discovery experiences, search functionality, and content continuation features.
- Develop and optimize retrieval and ranking systems, including candidate generation pipelines, embedding-based retrieval, two-tower architectures, ranking models, and serving infrastructure.
- Lead end-to-end experimentation efforts, including hypothesis development, A/B testing, performance analysis, and iterative model improvements.
- Improve recommendation quality across key challenges such as cold-start users, newly created content, creator discovery, and rapidly evolving content ecosystems.
- Build user, creator, content, and session-level representations using behavioral, contextual, and engagement signals.
- Collaborate closely with Product, Data Science, and Engineering teams to define success metrics and deliver measurable improvements in engagement, retention, satisfaction, and content distribution.
- Develop production-grade machine learning systems with robust monitoring, evaluation, scalability, and reliability standards.
- Contribute to the evolution of long-term machine learning infrastructure and architecture supporting AI-native content discovery experiences.
- Translate user behavior patterns and complex datasets into actionable machine learning solutions that enhance product performance and user experiences.
- Stay current with emerging advancements in recommendation systems, ranking methodologies, retrieval techniques, and AI-powered personalization technologies.
View Full Description & ApplyYou'll be redirected to the employer's site
