Sr. Software Engineer, ML Platform, 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, we’re 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 other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible. At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll 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 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
- Degree in a relevant field such as computer science, statistics, engineering, or equivalent experience
- 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 (Apply tot his job
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