Senior ML Platform Engineer — Real-Time Feature Pipelines

Cognitiv; San Mateo (CA); Hybrid; USD 190,000 - 250,000; Full time

Benefits offered by this job

Medical, Dental and Vision plans

12 weeks paid parental leave

Unlimited PTO

Hybrid work model

Equity for all employees

401(k) with employer match

Job summary

Cognitiv in San Mateo, CA is hiring a senior ML infrastructure engineer to design and scale feature pipelines and training systems for our advertising platform. You will own critical data capabilities, work with data scientists and engineers, and help deliver low-latency, high-throughput pipelines and reliable training workflows in a hybrid work setting.

Ideal candidates have 3-5+ years in ML infrastructure, strong programming skills (Python/Java/Scala), and hands-on experience with Spark,

Qualifications

• 3–5+ years of hands-on ML infrastructure / data platform experience.
• Proficiency in Python, Java, or Scala for building ML/data pipelines.
• Strong expertise with Spark, Flink, Kafka and distributed data processing.
• Excellent communication and cross-functional collaboration skills.

Responsibilities

• Architect, build, and maintain high-throughput feature pipelines and training workflows.
• Improve real-time and batch data pipelines and model training performance.
• Ensure system reliability, monitoring, and CI/CD across platforms.
• Collaborate with Modeling, Data Science, and Infrastructure teams.
• Translate signals into production-ready features to boost model performance.

Skills

ML infrastructure

Data pipelines

Python/Java/Scala

Distributed systems

Team collaboration

Tools

Apache Spark

Apache Flink

Apache Kafka

Kubernetes

Docker

AWS

GCP

Job description

Cognitiv in San Mateo, CA is hiring a senior ML infrastructure engineer to design and scale feature pipelines and training systems for our advertising platform. You will own critical data capabilities, work with data scientists and engineers, and help deliver low-latency, high-throughput pipelines and reliable training workflows in a hybrid work setting.

Ideal candidates have 3-5+ years in ML infrastructure, strong programming skills (Python/Java/Scala), and hands-on experience with Spark,