Senior Engineering Manager - ML

We are looking for an Engineering Manager in machine learning to lead a team of ML engineers in Bengaluru focused on recommendation systems. This is a leadership role; you will set technical direction, design systems, mentor engineers, and drive delivery of ML solutions that directly impact user engagement, retention, and Roku's revenue growth. The ideal candidate brings deep understanding of and passion for consumer-facing ML products and is ready to multiply their impact through a team.

Responsibilities:

• Lead, mentor, and grow a team of ML engineers; foster a culture of technical excellence, ownership, and collaboration.
• Set the technical roadmap, aligning priorities across the team and within the broader Recommendations organization.
• Drive system design and architecture decisions for ML models powering content ranking, user modeling, multi-objective optimization, and personalization across Roku's key surfaces.
• Provide technical leadership on model architecture choices, training and serving infrastructure, and evaluation methodologies.
• Own the A/B experimentation and measurement strategy for your team's surfaces; ensure ML work is tied to measurable product and business outcomes.
• Champion the adoption of generative AI to push the boundaries of recommendation and personalization.
• Partner with product, engineering, and cross-functional stakeholders to translate business goals into ML solutions.
• Recruit and develop ML talent in Bengaluru; establish strong engineering practices and a high hiring bar.
• Balance long-term research investments with near-term production improvements across multiple concurrent workstreams.

Requirements:

• 14+ years of experience in machine learning engineering, with a strong track record of shipping models to production in consumer-facing products (recommendations, search, ads, personalization, or similar domains).
• Experience managing an ML or software engineering team.
• BS/MS in Computer Science, Mathematics, Statistics, or a related quantitative field; a PhD is a plus.
• Deep expertise in recommendation system architectures, deep neural networks, and ranking models.
• Strong software engineering fundamentals and experience writing production-quality code.
• Experience with large-scale ML tooling and infrastructure: PyTorch/TensorFlow, Spark, Airflow, and cloud-native MLOps platforms.
• Experience with multi-objective optimization, reinforcement learning, or Bayesian methods in production settings.
• Familiarity with LLM-based approaches for recommendations, content understanding, or generative personalization.
• Demonstrated ability to connect ML work to measurable product and business outcomes.
• Experience building and scaling ML teams in a distributed or multi-site setting.