Manager, Machine Learning Engineering – Ad Platforms

Responsibilities

• Lead, mentor and guide Data Scientists, Machine Learning and AI engineers to build solutions adhering to industry best practices and deliver scalable solutions including model architecture and algorithm selection.
• Lead by example and always strive to improve the design for more scalable, cleaner, and decoupled implementations.
• Drive adoption of best practices in model development, code quality, testing, and documentation.
• Have a solid understanding and usage of automated tools (AI) while adhering to company policy.
• Define strategic direction for machine learning projects and collaborate with product and engineering stakeholders.
• Oversee end‑to‑end machine learning workflow, including data collection, model development, deployment and modeling.
• Foster innovation by exploring new ML techniques, tools, and technologies.
• Communicate strategies, progress, and results to leadership and cross‑functional teams.
• Ensure responsible AI practices, including fairness, explainability, and compliance with privacy and ethical standards.
• Develop partnerships across the organization to identify and prioritize high‑impact ML opportunities.
• Be available for on‑call rotations based on the team’s escalation policy and support schedule for ML/AI solutions.

• Bachelor’s or master’s degree in computer science, engineering, mathematics, statistics, or a related field.
• 8+ years of relevant industry experience, with at least 2‑3 years in a people‑management or technical leadership role.
• Proven ability to translate business problems into scalable ML and GenAI solutions and a strong understanding of machine learning fundamentals, deep learning, and statistical modeling.
• Proven experience designing, building, and deploying scalable machine learning models and systems in production.
• Experience deploying ML/GenAI systems at scale using cloud platforms and MLOps practices.
• Advanced programming proficiency (e.g., Python, Java, or similar); experience with ML/DL frameworks (e.g., Tensor Flow, PyTorch, JAX, Hugging Face).
• Experience building, fine‑tuning, evaluating, and deploying LLM‑based systems (e.g., RAG, prompt engineering, model optimization).
• Demonstrated ability to lead global teams and collaborate across organizational boundaries.