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, weâre 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 otherâs unique experiences and embrace the flexibility to do your best work. Creating a career you love? Itâs Possible.
At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and weâre looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, weâll 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 Pinterestâs next generation of realtime ML and measurement infrastructure, with a focus on subâmillisecond decisioning, highâthroughput data access, and tight integration with Pinterestâs core tech stack.
In this role, youâll think about queries and RPCs in terms of syscalls, cache lines, and wire formats, and design systems that stay fast and predictable under load. Youâll 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. Youâll 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, lockâfree or lowâcontention data structures, connection pooling, query planning, kernel and network tuning, onâdisk layout and indexing, circuitâbreaking, autoscaling, incident response, NixOS, Rust, and robust SLIs/SLOs sound interesting (even if itâs just a subset), this role gives you a chance to apply that expertise to businessâcritical, highâleverage 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 (<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 our PinFlex page to learn more about our working model.
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At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise.
Information regarding the culture at Pinterest and benefits available for this position can be found here.
Our Commitment to Inclusion: