The opportunity
As a Senior Software Engineer II in the Ads Quality team specializing in ML infrastructure, you'll work with a team of software and machine learning engineers to build state-of-the-art systems that optimize Ads performance throughout the ads serving funnel, from retrieval to…
What you'll do
Design, develop, and deploy machine learning infrastructure to tackle: practical challenges in our complex marketplace while advancing the training and serving platform. Work with the company wide ML Foundation team to develop solutions to meet Ads unique needs.
Optimize training and serving funnel bottlenecks—profiling real systems and: driving meaningful wins in throughput, cost, and latency, from GPU/accelerator utilization to tail latency at serving time.
Identify instabilities and shortcomings of our systems and operating: procedures to deliver best in class availability.
Build real-time feature processing infrastructure—low-latency pipelines that: compute, join, and serve fresh features to models on the critical path of every ad request.
Lead the infrastructure foundation for our real-time sequential foundation: model, including online sequence feature stores, streaming update paths, efficient long-context serving, and training pipelines that keep pace with rapidly evolving data.
Collaborate closely with product managers, data scientists, and MLEs to: deeply understand business needs and co-design systems where product, model architecture and infrastructure constraints are solved together.
What they're looking for
- Have 5+ years of industry experience tech leading a team to build ML: infrastructure or using machine learning to solve real-world problems with large datasets.
- Demonstrated experience solving complex model training and serving efficiency: challenges, including optimizing training pipelines, reducing model training times, and improving serving architectures to enhance operational efficiency and scalability.
- Hands-on experience with real-time / online feature processing and: serving—streaming systems (e.g., Kafka, Flink), online feature stores, and low-latency retrieval on the request path.
- Experience with sequential modeling, transformer architecture, and generative: retrieval or building large scale online recommendation systems.
- Experience serving large sequential or foundation models in low-latency, high-QPS production settings.
- Experience in digital advertising platforms.
- Familiarity with LLM integrations, prompt engineering, and productivity tooling.
