Senior Machine Learning Engineer, Features
$190,000–$250,000 year
HybridSan Mateo, Calabarzon, Philippines
Job Summary
Architect, build, and maintain low-latency, high-throughput feature pipelines for real-time bidding systems, including batch, streaming, and point-in-time correct historical data. Leverage LLMs and deep learning models to extract contextual embeddings into the core feature store, optimizing generation and retrieval for sub-millisecond serving SLAs. Own and enhance ad model training pipelines to improve speed, resource utilization, and throughput for large-scale deep learning models. Establish technical standards for monitoring, testing, and CI/CD across infrastructure to ensure robust system SLAs. Partner with modeling teams to translate complex signals into production-ready features that boost CTR and CVR predictions. Work onsite Monday through Wednesday in San Mateo, CA with a hybrid schedule.
Required Qualifications
- 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
Desired Qualifications
- Domain expertise in AdTech
- Hands-on experience with PyTorch for model architecture, training pipeline acceleration, or distributed training
- Proficiency with cloud & infrastructure technologies, including AWS/GCP, as well as containerization and orchestration platforms like Kubernetes (K8s) and Docker
- Competitive programming background, such as awards or achievements in OI (Olympiad in Informatics) or ACM/ICPC
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