Senior Machine Learning Infrastructure Engineer role at Cognitiv in San Mateo, CA with hybrid work schedule (3 days in office, 2 days remote optional). Responsibilities include architecting and maintaining scalable feature pipelines for real-time bidding systems, integrating embeddings and generative AI, optimizing ad model training pipelines, ensuring system scalability and reliability, and collaborating with modeling and data science teams. Requires 3-5+ years experience in ML infrastructure or data platforms, proficiency in Python, Java, or Scala, expertise in big data technologies (Spark, Flink, Kafka), and domain knowledge in AdTech. Nice to have experience with PyTorch, cloud platforms (AWS/GCP), Kubernetes, Docker, and competitive programming background. Salary range $175,000-$225,000 plus equity. Benefits include medical, dental, vision, parental leave, unlimited PTO, equity, 401(k)/RRSP match, and more.
What you'll do
Architect, build, and maintain low-latency, high-throughput feature pipelines — batch, real-time streaming, and point-in-time correct historical features — to power real-time bidding systems
Similar jobs
More roles worth a look
Related opportunities based on specialty and working model so candidates can keep momentum.
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
Own and continuously enhance ad model training pipelines, improving training speed, resource utilization, and throughput for large-scale deep learning models
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 teams to translate complex signals into production-ready features that directly boost model performance (e.g., CTR/CVR prediction)
Independently own core feature pipelines, embedding services, or training subsystems
Deliver measurable improvements such as reduced serving latency, faster training throughput, or expanded monitoring/CI-CD coverage
Redesign or scale key systems to handle growing data volume or model complexity
Operate autonomously as a trusted technical partner to Modeling, Data Science, and Engineering teams
Requirements
Strong fundamentals and first principles thinking
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
Domain expertise in AdTech
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
Nice to have: Hands-on experience with PyTorch for model architecture, training pipeline acceleration, or distributed training
Nice to have: Proficiency with cloud & infrastructure technologies, including AWS/GCP, containerization and orchestration platforms like Kubernetes and Docker
Nice to have: Competitive programming background (awards or achievements in OI or ACM/ICPC)
Tech stack
PythonJavaKafkaFlinkSparkPyTorchKubernetesAWS
Benefits
Medical, Dental and Vision plan for US employees & Extended Health Benefits for Canadian employees12 weeks paid parental leave + 4 weeks work-from-homeUnlimited PTO + Work-From-Anywhere AugustCareer development with clear advancement pathsEquity for all employeesHybrid work model & daily team lunchHealth & wellness stipend + cell phone reimbursement401(k) & RRSP with employer matchParking (CA, WA, Vancouver offices) & pre-tax commuter benefitsEmployee Assistance ProgramComprehensive onboarding (Cognitiv University)
Apply now
Ready to take the next step in your career? Click the button below to continue to the application process.
