机器学习工程经理

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

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

Track record of building recommendation systems that directly influenced product metrics at significant scale

Experience with state-of-the-art recommendation system architectures including deep learning, transformer-based models, and neural collaborative filtering

Experience with large-scale personalization systems, user modeling, and multi-objective ranking optimization

Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)

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)

Track record of implementing SOTA research papers into production recommendation and ranking systems

Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies

Experience managing teams working on large-scale recommendation, ranking, or retrieval systems in a production environment

Hands-on background in ML model development using frameworks such as PyTorch or TensorFlow, with specific experience in recommendation models

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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.