ML Platform Engineer, tvScientific
About Pinterest: Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product. Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible. At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI. Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here. About tvScientific tvScientific is the first and only CTV advertising platform purpose-built for performance marketers. We leverage massive data and cutting-edge science to automate and optimize TV advertising to drive business outcomes. Our solution combines media buying, optimization, measurement, and attribution in one, efficient platform. Our platform is built by industry leaders with a long history in programmatic advertising, digital media, and ad verification who have now purpose-built a CTV performance platform advertisers can trust to grow their business. We are looking for an ambitious Systems / Platform Engineer to join a team at the intersection of SRE and low-latency distributed systems. This team will help power Pinterest’s next generation of realtime ML and measurement infrastructure, with a focus on sub‑millisecond decisioning, high‑throughput data access, and tight integration with Pinterest’s core tech stack. In this role, you’ll think about queries and RPCs in terms of syscalls, cache lines, and wire formats, and design systems that stay fast and predictable under load. You’ll help define and harden the foundation for our training and serving stack: from storage and indexing strategies, to streaming and fanout, to backpressure and failure handling across services and regions. You’ll work closely with software engineering, data infra, and SRE partners to ensure our systems are observable, debuggable, and operable in production. If topics like IO scheduling and batching, lock‑free or low‑contention data structures, connection pooling, query planning, kernel and network tuning, on‑disk layout and indexing, circuit‑breaking, autoscaling, incident response, NixOS, Rust, and robust SLIs/SLOs sound interesting (even if it’s just a subset), this role gives you a chance to apply that expertise to business‑critical, high‑leverage infrastructure at Pinterest scale. What you'll do: Scale the decision making process for tools for the tvScientific AI team, from our workflows to our training infrastructure to our Kubernetes deployments Improve the developer experience for the data science team Upgrade our observability tooling Make every deployment smooth as our infrastructure evolves. What we're looking for: Deep understanding of Linux Excellent writing skills A systems-oriented mindset Experience in high-performance software (RTB, HFT, etc.) Software engineering experience + reliability (e.g. CI/CD) expertise Strong observability instincts Demonstrated ability to use AI to improve speed and quality in your day-to-day workflow for relevant outputs Strong track record of critical evaluation and verification of AI-assisted work (e.g., testing, source-checking, data validation, peer review) High integrity and ownership: you protect sensitive data, avoid over-reliance on AI, and remain accountable for final decisions and deliverables Nice-To-Haves Reverse-engineering experience Terraform, EKS, or MLOps experience Python, Scala, or Zig experience NixOS experience Adtech or CTV experience Experience deploying a distributed system across multiple clouds Experience in hard real-time low-latency (Apply To This Job