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 were 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 others unique experiences and embrace theflexibility to do your best work. Creating a career you love Its Possible.

At Pinterest AI isnt just a feature its a powerful partner that augments our creativity and amplifies our impact and were looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities well 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 Pinterests next generation of realtime ML and measurement infrastructure with a focus on submillisecond decisioning highthroughput data access and tight integration with Pinterests core tech stack.

In this role youll think about queries and RPCs in terms of syscalls cache lines and wire formats and design systems that stay fast and predictable under load. Youll 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. Youll 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 lockfree or lowcontention data structures connection pooling query planning kernel and network tuning ondisk layout and indexing circuitbreaking autoscaling incident response NixOS Rust and robust SLIs/SLOs sound interesting (even if its just a subset) this role gives you a chance to apply that expertise to businesscritical highleverage infrastructure at Pinterest scale.

What youll 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 were 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 (<10 ms) environments

In-Office Requirement Statement:

• We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.

Relocation Statement:

• This position is not eligible for relocation assistance. Visit ourPinFlexpage to learn more about our working model.

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