Sr. Software Engineer, Machine Learning, tvScientific

San Francisco + 1 other locations Remote

Found: Today

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.

As a Sr. Machine Learning Engineer at tvScientific, you'll build the ML and AI systems behind our Connected TV ad-buying platform: real-time bidding, campaign optimization, and incrementality measurement at scale. We're an adtech company solving a hard problem: making CTV advertising actually measurable. Our platform helps advertisers buy ads across the CTV ecosystem: Hulu, Pluto TV, Disney+, HBO Max, and hundreds of FAST channels: and prove that those ads drove real business outcomes.

What you'll do:

Write production Python that powers real-time bidding, model training, and campaign optimization
Train, deploy, and monitor ML models that decide which ads to show, when, and at what price: millions of bid decisions per second
Build and improve our incrementality measurement systems: helping advertisers understand the true causal lift of their CTV spend
Design and implement new ML products across the ad-buying lifecycle: audience targeting, bid optimization, pacing, and attribution
Use LLMs and generative AI to build internal tools that accelerate how we develop, test, and ship ML systems
Serve as a technical lead and mentor on a distributed engineering team

What we're looking for:

Strong production Python skills: you write code that runs in prod, not just notebooks
Solid statistics and ML fundamentals: you can reason about experiment design, model evaluation, and when simpler approaches beat complex ones
Familiarity with modern AI tools and good judgment about where they add value
Adtech or CTV experience: familiarity with RTB, programmatic advertising, supply-path optimization
Clear written communication: we're a distributed team and writing is how decisions get made
Comfort with ambiguity: you'll own problems end-to-end in a fast-moving environment, from scoping to shipping
Bachelor's degree in Computer Science, Mathematics, Engineering, related field, or equivalent experience
4+ years of industry experience
Nice-to-Haves:
- Experience using Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring
- Familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL/data exploration, and engineering workflow acceleration
- Causal inference: uplift modeling, synthetic controls, difference-in-differences, or incrementality testing
- Big data experience with Scala and Spark
- Systems programming experience in Zig or similar (C, C++, Rust)
- Reinforcement learning or bandit algorithms in production
- Experience building agentic AI systems or LLM-powered workflows
- MLOps experience: model deployment, monitoring, and pipeline orchestration on AWS

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: