About the job
tvScientific, powered by Pinterest, develops a connected TV (CTV) advertising platform designed for performance marketers. The platform combines media buying, optimization, measurement, and attribution to automate and improve TV advertising. Built by professionals in programmatic advertising, digital media, and ad verification, tvScientific aims to deliver measurable results for advertisers.
Role overview
As a Machine Learning Platform Engineer, you will join a team that operates where Site Reliability Engineering meets low-latency distributed systems. This team advances Pinterest’s real-time machine learning and measurement infrastructure, focusing on sub-millisecond decision-making and high-throughput data access. Seamless integration with Pinterest’s core stack is central to the work.
What you will do
- Design and build systems to keep queries and RPCs fast and reliable, even during periods of heavy demand.
- Develop and enhance the foundation of the machine learning training and serving stack.
- Address challenges in storage, indexing, streaming, fan-out, and managing backpressure and failures across services and regions.
- Collaborate with software engineering, data infrastructure, and SRE teams to ensure systems are observable, debuggable, and ready for production.
Key areas of focus
- I/O scheduling and batching
- Lock-free or low-contention data structures
- Connection pooling and query planning
- Kernel and network tuning
- On-disk layout and indexing strategies
- Circuit-breaking and autoscaling
- Incident response and failure management
- NixOS
- Defining and maintaining SLIs and SLOs
This position is a strong fit for engineers interested in building and operating large-scale infrastructure, particularly those who enjoy working on real-time systems, observability, and reliability.

