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Responsibilities
- Manage multiple teams of ML engineers and technical leaders delivering large-scale recommendation and ranking systems across model development, training, evaluation, and production serving
- Drive the technical strategy and roadmap for MRS initiatives, influencing decisions around SOTA model architectures, recommendation algorithms, and deployment approaches
- Actively engage with technical direction and code quality across teams, staying current with state-of-the-art research in recommendation systems and applying cutting-edge techniques
- Partner with product, data science, and research to define recommendation system problem formulations, prioritize ranking experiments, and translate model improvements into measurable product outcomes
- Recruit, develop, and retain ML engineers and engineering leaders with deep expertise in recommendation systems and ranking models
- Champion adoption of SOTA techniques in recommendation systems, including deep learning approaches, transformer-based models, and multi-objective optimization
- Proactively identify and resolve execution risks across recommendation system projects, including data quality issues, model performance regressions, training instability, and infrastructure bottlenecks
- Establish a team culture that values code quality, rigorous experimentation practices, and continuous learning from recommendation systems research
- Hold leaders accountable for performance, technical depth in ranking and personalization, and cross-functional engagement
- Represent the team's work and priorities to leadership, communicating recommendation system tradeoffs, SOTA advances, and strategic implications clearly
Minimum Qualifications
- 8+ years of experience in software engineering with a focus on machine learning systems, including model development, training pipelines, or ML infrastructure
- 4+ years of experience managing engineering teams, including experience managing other engineering leaders
- Experience driving technical strategy and roadmap decisions for ML systems across the full model lifecycle, from data ingestion through production serving
- Experience partnering cross-functionally with product, data science, and research teams to define ML problem scope and deliver measurable outcomes
- Experience recruiting, developing, and retaining ML engineering talent and building high-performing teams in ambiguous, high-impact areas
Preferred Qualifications
- Hands-on background in ML model development using frameworks such as PyTorch or TensorFlow, with specific experience in recommendation models
- Experience managing teams working on large-scale recommendation, ranking, or retrieval systems in a production environment
- Experience with large-scale personalization systems, user modeling, and multi-objective ranking optimization
- Track record of building recommendation systems that directly influenced product metrics at significant scale
- Track record of implementing SOTA research papers into production recommendation and ranking systems
- Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
- Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
- Demonstrated ability to evaluate and adopt emerging techniques from top ML conferences (RecSys, KDD, NeurIPS, ICML) into production systems
- Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
- Experience with state-of-the-art recommendation system architectures including deep learning, transformer-based models, and neural collaborative filtering
$219,000/year to $301,000/year + bonus + equity + benefits
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