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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 experienced ML Platform Engineer to join a team at the intersection of sysops, systems programming, architecture, and large-scale deployments. Our platform underpins tvScientific's distributed real-time bidding agent and ML training system that together drive $100M+ in annual revenue, giving you the opportunity to work on some of the most business-critical infrastructure in the company.

As part of our team, you'll think about datasets in terms of bytes, microseconds, and serialization formats, and help define the next generation of our training and serving stack. A flagship initiative for the coming year is building a Kubernetes + Ray backend for our model training pipelines, setting a new bar for scale and reliability. If topics like data locality, observability and anomaly detection, distributed databases, high-performance computing, array programming languages, data security, and reproducibility excite you (even if it's just a subset), your expertise could play a key role in shaping tvScientific's ML innovation in 2026 and beyond.

What You'll Do

• Scale the decisionmaking 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.

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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