Software Engineer II, ML Ops, 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.

In this role youll work at the intersection of SRE and low-latency distributed systems with plenty of room to go deep on complex technical problems. Youll help build and operate the platform that powers AI model training deployment and serving contributing to meaningful production-facing projects from day one.

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 interact with IO scheduling and batching lock-free and 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.

What youll do

• Contribute to the infrastructure supporting AI workflows training pipelines Kubernetes deployments and CI/CD.
• Help improve the developer experience for the data science team small frictions add up and youll help eliminate them.
• Build out and improve observability tooling learning to see the system clearly is a core skill well develop together.
• Keep deployments clean and correct as the platform evolves.
• Grow into a deeper technical contributor under the mentorship of senior engineers who have done this at high scale.

What were looking for

• A genuine demonstrable depth in Linux hands-on experience beyond basic usage (for example debugging configuration or performance tuning).
• Strong software engineering fundamentals you write clean code reason about systems and debug methodically.
• A systems-oriented mindset you think about why things work not just that they work.
• Early exposure to reliability concepts CI/CD infrastructure-as-code or similar.
• An ownership mindset especially when diagnosing and resolving production or project issues.
• Comfort using AI tooling to accelerate your work with the discipline to verify what it produces.
• Demonstrated ability to use AI to improve speed and quality in your day-to-day workflow for relevant outputs.
• A track record of critically evaluating and validating AI-assisted work (for example 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.
• 2 years of experience building and operating high-performance distributed systems.
• Bachelors degree in computer science engineering a related field or equivalent experience.
• Nice-to-haves
  • Experience with NixOS or other tools for reproducible builds and an interest in making development environments predictable and reliable.
  • Experience with Zig or similar low-level languages and curiosity about what your compiler and runtime are doing under the hood.
  • Youve reverse-engineered something a protocol a binary a game etc.
  • Youve deployed something real to Kubernetes even if it was a homelab.
  • Experience with Terraform or other infrastructure-as-code tools in a real context.
  • Exposure to adtech CTV or other high-performance/low-latency environments.
  • Python or Scala experience in a data-adjacent context.

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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