Sr. ML Ops Engineer, tvScientific

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.

tvScientific is looking for a Senior MLOps Engineer!

Youll be working with a distributed engineering team on our Connected TV ad-buying platform, as we scale our Machine Learning practice. We've cracked the code on optimizing TV ad campaigns. We're scaling massively and we need your help to make that scale sustainable.

An Idealab company, tvScientific was co-founded by executives with deep roots in programmatic advertising and digital media. tvScientific helps our clients buy ads across the CTV universe, from Hulu to PlutoTV to the ad-supported tier of Disney+ and (HBO) Max. Since our acquisition by Pinterest, we're expanding our work on CTV to lift search & social advertising performance.

Job Description

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
• Serve as a technical lead and mentor to the team
• Make every deployment smooth as our infrastructure evolves

Requirements

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

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

Work Environment

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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