Engineering Manager - Machine Learning (Recommendation Systems)

Role Name: Engineering Manager - Machine Learning (Recommendation Systems)

About the job As a Engineering Manager in ML team, you will lead and scale our ML team while driving the technical vision for our recommendation infrastructure. You'll be responsible for building and mentoring a high-performing team, establishing MLOps practices, and advancing our recommendation algorithms for our Audio Stories and Podcasting Platform.

Responsibilities

• Team Leadership: Build, mentor, and grow a team of ML engineers. Define team processes, establish best practices, and foster a culture of innovation and continuous learning.
• Technical Strategy: Define and execute the technical roadmap for recommendation systems, including infrastructure decisions, model architecture choices, and MLOps practices.
• MLOps Infrastructure: Design and implement robust ML pipelines for model training, deployment, and monitoring. Establish practices for continuous integration/deployment of ML models, A/B testing frameworks, and automated performance monitoring.
• Algorithm Development & Innovation: Guide the team in designing and implementing state-of-the-art deep learning models for content understanding, retrieval, and ranking. Drive architectural decisions for scaling recommendation systems.
• System Design: Architect scalable solutions for real-time inference, ensuring high availability and low latency of recommendation services. Design systems for efficient model serving and feature computation.
• Cross-functional Leadership: Collaborate with engineering, product, and business leaders to align ML initiatives with company objectives. Drive technical decisions that balance innovation with business impact.
• Production Excellence: Establish metrics and monitoring systems for model performance, system health, and business KPIs. Lead incident response and optimization efforts for production ML systems.
• Research Direction: Set the research agenda for the team, prioritizing high-impact areas for innovation. Foster collaboration with academic institutions and research communities.

Requirements

• Experience: 6+ years of ML engineering experience, with 3+ years in team leadership roles
• Education: Master's or Ph.D. in Computer Science, Machine Learning, or related field
• Technical Leadership:
• Proven track record of building and leading ML teams
• Experience setting technical direction for large-scale ML systems
• Strong background in MLOps practices and production ML system design
• Technical Expertise:
• Deep expertise in recommendation systems, ranking algorithms, and deep learning
• Experience with large-scale distributed systems and real-time serving architectures
• Strong knowledge of ML infrastructure, including feature stores, experiment platforms, and model serving systems
• Proficiency in Python, SQL, and ML frameworks (PyTorch, TensorFlow)
• Business Impact:
• Track record of delivering measurable business impact through ML initiatives
• Experience translating business requirements into technical solutions
• Communication & Leadership:
• Outstanding communication and stakeholder management skills
• Experience mentoring engineers and developing technical talent
• Ability to influence cross-functional stakeholders and drive consensus
• Publication/Patents: Publications in top-tier conferences (NIPS, ICML, RecSys) or patents in recommendation systems are a plus
• Must have experience with Recommendation Systems for a OTT Platform

This position offers the opportunity to shape the future of audio content discovery while building and leading a world-class ML team.Role & responsibilities