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Machine Learning Platform Engineer

tvScientific powered by PinterestSan Francisco, CA, US; Remote, US
Remote Full-time $123.7K/yr - $254.7K/yr

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Experience Level

Entry Level

Qualifications

Responsibilities: Enhance the decision-making processes for tools used by the tvScientific AI team, covering workflows, training infrastructure, and Kubernetes deployments. Elevate the developer experience for our data science team. Upgrade and refine our observability tools. Ensure seamless deployments as our infrastructure evolves. Qualifications: In-depth knowledge of Linux operating systems. Exceptional writing and communication skills. A strong systems-oriented mindset. Experience with high-performance software (e.g., Real-Time Bidding, High-Frequency Trading). Solid background in software engineering and reliability (e.g., CI/CD practices).

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.

About tvScientific powered by Pinterest

tvScientific stands at the forefront of the CTV advertising landscape, uniquely designed for performance marketers. Our platform, built on extensive data analytics and advanced scientific methodologies, empowers businesses to optimize their advertising efforts and achieve measurable outcomes.

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