Machine Learning Platform Engineer

About the Role  

At PubMatic, data operates at unmatched scale. As a Senior Machine Learning 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-functionally 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.