Senior Machine Learning Engineer, Features

High-Performance Feature Engineering & Infrastructure: Architect, build, and maintain low-latency, high-throughput feature pipelines — batch, real-time streaming, and point-in-time correct historical features — to power our real-time bidding systems.

Advanced Embeddings & Generative AI Integration: Leverage LLMs and deep learning models to extract rich contextual and user-level embeddings into the core feature store/serving system, optimizing embedding generation, indexing, and online retrieval for sub-millisecond serving SLAs.

Model Training Pipeline Optimization: Own and continuously enhance Cognitiv's ad model training pipelines, improving training speed, resource utilization, and throughput for large-scale deep learning models.

System Scalability, Reliability & Efficiency: Establish technical standards for monitoring, testing, and CI/CD across feature and training infrastructure to ensure robust system SLAs/SLOs.

Partner with Modeling & Data Science: Translate complex signals into production-ready features that directly boost model performance (e.g., CTR/CVR prediction).

Must haves:

3-5+ years of hands-on Machine Learning Infrastructure / Data Platform experience supporting data-intensive platforms, including large-scale data pipelines, streaming systems, and storage layers.

Proficiency in one or more core programming languages — Python, Java, or Scala — for building, maintaining, and scaling robust ML and data pipelines.

Strong expertise in modern big data technologies such as Apache Spark, Apache Flink, Apache Kafka, and other distributed data processing frameworks.

Excellent team communication, cross-functional collaboration, and problem-solving skills, with a track record of partnering effectively with modeling and engineering teams.

Willing to work onsite Monday/Tuesday/Wednesday in San Mateo, CA.

Nice to haves: