Senior Staff Engineer, Machine Learning Platform

About The Role At PubMatic, data operates at an unmatched scale. As a Senior ML Platform Engineer, you will design and scale the infrastructure and frameworks that enable machine learning development, experimentation, and production across a global ecosystem, handling trillions of ad impressions. You will collaborate closely with ML Engineers, Data Scientists, and Product stakeholders to accelerate experimentation, maximize efficiency, and translate AI solutions from concept to production. This role offers direct exposure to petabyte-scale datasets, industry-standard ML efficiency tools (e.g., Triton inference, GPU/accelerated computing), and the opportunity to evaluate and adopt emerging AI/ML technologies. You will contribute to the next-generation ML platform for adtech, enabling advanced use cases such as troubleshooting issues in bid stream, competitive intelligence, benchmarking, forecasting, reinforcement learning, and retrieval-augmented generation (RAG), while also establishing foundational capabilities like embeddings and observability frameworks.

What You’ll Do

• Platform Development: Design and maintain scalable ML pipelines and platforms for ingestion, feature engineering, training, evaluation, inference, and deployment.
• Big Data & Analytics: Build and optimize large-scale data workflows using distributed systems (Spark, Hadoop, Kafka, Snowflake) to support analytics and model training.
• Experimentation & Observability: Develop frameworks for experiment tracking, automated reporting, and observability to monitor model health, drift, and anomalies.
• Work with industry-standard ML efficiency tools to optimize training workloads, accelerate experiments, and monitor performance at scale.
• AI/ML Enablement: Provide reusable components, SDKs, and APIs that empower teams to leverage AI insights and ML models effectively.
• Automation: Drive CI/CD, workflow orchestration, and infrastructure-as-code practices for ML jobs, ensuring reliability and reproducibility.
• Collaboration: Partner cross-functional with Product, Data Science, and Engineering teams to align ML infrastructure with business needs.
• Innovation: Stay ahead of emerging trends in Generative AI, ML Ops, and Big Data to introduce best practices and next-gen solutions.
• Impact & Growth Opportunities: Work with petabyte-scale datasets and billions of transactions, powering global AdTech.
• Apply AI/ML to deal troubleshooting, competitive intelligence, benchmarking, forecasting, and actionable insights.
• Build advanced frameworks such as RAG systems, reinforcement learning strategies, and embedding platforms.
• Convert business challenges into ML products, pioneering industry-first solutions.
• Gain hands-on exposure to GPU/accelerated computing, Triton inference, and modern ML Ops frameworks.
• Advance your career with a clear growth path into applied ML engineering and research.
• Be part of a culture that values experimentation, thought leadership, and cross functional collaboration.

We’d Love for You to Have

• Experience of 5 to 10 years of hands on experience with petabyte-scale datasets and distributed systems.
• Data Analytics: Proficiency in SQL, Python (pandas, NumPy), and analytics/BI tools for data exploration and monitoring.
• Private Marketplace (PMP) & Deal Optimization: Strong understanding of Programmatic Advertising and Private Marketplace (PMP) ecosystems, including deal performance analytics, audience targeting, inventory optimization, and KPI driven marketplace optimization.
• Familiarity with BI tools such as Looker or Grafana.
• A passion for applied AI/ML and eagerness to bring research ideas into production.
• Big Data: Strong expertise with Spark, Hadoop, Kafka, and data warehousing (Snowflake, SparkSQL).
• ML Ops/Infrastructure: Experience with CI/CD, Docker, Kubernetes, Airflow/MLflow, and experiment tracking tools.
• Programming: Skilled in Python/Scala/Java for data-intensive applications.
• Problem Solving: Strong analytical skills and ability to debug complex data/ML pipeline issues.
• Collaboration: Excellent communication and teamwork skills in cross-functional settings.

Qualifications

• Education: Bachelor’s or master's in computer science, Data Engineering, or related field