Senior Machine Learning Engineer

Overview

I have partnered with a leading AI-driven advertising technology company to hire a Senior Machine Learning Engineer who will play a pivotal role in building and scaling distributed machine learning infrastructure that powers real-time programmatic decisioning. This role sits at the intersection of proprietary advertiser data, predictive analytics, and large-scale ML systems, supporting a platform that deploys custom bidding algorithms across major DSPs and walled gardens.

This is a highly technical, impact-driven engineering role, not a research-only or analytics position. You will own the end-to-end lifecycle of production ML systems, from distributed training and hyperparameter tuning to batch inference and observability. Working closely with engineering, product, and data science leadership, you will advance core AI products by translating sophisticated models into scalable, reliable systems that directly influence product strategy and business outcomes in the programmatic advertising ecosystem.

Senior Machine Learning Engineer - Responsibilities

• Design and deploy scalable, distributed ML systems for audience modeling and bid optimization, leveraging PyTorch and Ray on Databricks to support multi-GPU training, hyperparameter tuning, and champion/challenger model evaluation.
• Own end-to-end ML pipelines from feature engineering and model training to batch inference, ensuring automation, reproducibility, fault tolerance, and reliable checkpoint recovery in production environments.
• Build and operate robust MLOps and observability frameworks, implementing model versioning, experiment tracking, drift detection, and performance monitoring (AUC, AUPRC, F1) using MLflow and modern monitoring stacks.
• Collaborate cross-functionally with product, data science, and engineering leadership to align ML development with core product strategy and business KPIs across multiple AI-driven advertising products.
• Provide technical leadership and architectural guidance, driving best practices in distributed ML infrastructure, contributing to shared codebases, documenting systems, and enabling teams to extend and scale the ML platform.

Senior Machine Learning Engineer - Requirements

• Advanced academic background (MS or PhD) in Computer Science, Statistics, Machine Learning, or a related field, with 5-10 years of industry experience building and deploying production ML systems at scale.
• Deep expertise in PyTorch and distributed ML training, including custom neural network architectures (embeddings, MLPs, classification heads), multi-GPU workflows, and batch inference pipelines with robust artifact and schema management.
• Strong production experience on Databricks, leveraging Delta Lake, Unity Catalog, and cluster management to support scalable training, feature engineering, and governed ML lifecycles.
• Proven MLOps and software engineering rigor, encompassing MLflow-based experiment tracking and model versioning, CI/CD workflows, monitoring and observability, and reproducible ML systems built with Python and PySpark.
• Cloud-native and collaborative engineering mindset, with hands-on experience across AWS and modern data platforms, and the ability to partner effectively with Product, Data Science, and Engineering teams to deliver business-critical ML solutions.
  

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