Machine Learning Engineer

This role designs and builds scalable real-time data pipelines and ad optimization models for a high-throughput digital advertising platform. The Senior/Staff ML Engineer works with Data Scientists and Engineering teams on microservice architectures running training, inference, and monitoring on thousands of ML models concurrently. Requires deep understanding of algorithm design, concurrency, and experience implementing probabilistic or machine learning algorithms.

Develops and deploys machine learning models to optimize ad targeting and bidding algorithms. Requires strong ML engineering skills and experience with large-scale data.

This senior individual-contributor role focuses on architecting and maintaining high-performance feature pipelines (batch, streaming, point-in-time correct) for Cognitiv's real-time bidding systems. The engineer will own model training pipeline optimization, integrate LLM/deep learning embeddings into the feature store, and establish technical standards for monitoring and CI/CD. Requires 3-5+ years of ML infrastructure experience, proficiency in Python/Java/Scala, and deep expertise in AdTech and big data technologies like Spark, Flink, and Kafka.

This role designs and implements machine learning models and systems, optimizing algorithms for real-world applications. It requires strong software engineering skills and experience with ML frameworks.

Lead engineering for the Karlo platform, owning technical architecture, system design, and delivery of new platform features. Drive innovation and collaborate closely with product and design teams to align technical roadmap with business goals and ensure platform scalability and reliability.

Build and maintain scalable data pipelines and analytics infrastructure to power advertising insights and reporting. Collaborate with data scientists and engineers to ensure data quality, reliability, and accessibility for business intelligence and machine learning use cases.

This role manages the revenue and marketing technology stack, overseeing system integrations, data accuracy, and process optimization to drive operational efficiency. The manager will focus on aligning marketing and sales systems to improve data flow and support revenue growth initiatives.