Manager, Machine Learning Engineering, Ad Platforms

What you'll do

• Lead and mentor Data Scientists and ML engineers to build scalable model architectures and algorithms.
• Drive the adoption of industry best practices for model development, code quality, testing, and documentation.
• Oversee the end-to-end machine learning workflow including data collection, model development, and deployment.
• Translate product requirements into well-defined technical implementations and define operational metrics to measure system health.
• Define the strategic direction for machine learning projects in collaboration with product and engineering stakeholders.
• Ensure responsible AI practices including fairness, explainability, and compliance with privacy and ethical standards.
• Identify and prioritize high-impact ML opportunities by developing partnerships across the organization.
• Participate in on-call rotations to support ML/AI solutions based on the team's escalation policy.

What we're looking for

• Bachelor’s or master’s degree in computer science, Engineering, Mathematics, Statistics, or a related field.
• 8+ years of relevant industry experience.
• 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 with strong understanding of ML 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 in Python, Java, or similar, and experience with ML/DL frameworks like TensorFlow, PyTorch, JAX, or Hugging Face.
• Experience building, fine-tuning, evaluating, and deploying LLM-based systems including RAG, prompt engineering, and model optimization.
• Demonstrated ability to lead global teams and collaborate across organizational boundaries.
• Domain knowledge in the Ad Tech industry (preferred).
• Experience working with large-scale data and distributed systems (preferred).
• Knowledge of cloud platforms (e.g., AWS, GCP, Azure) and MLOps pipelines (preferred).
• Track record of innovation and contributions to the ML/AI community (preferred).