# Sr. Software Engineer, Machine Learning, tvScientific

**Company**: tvScientific
**Location**: San Francisco, CA
**Work arrangement**: remote
**Experience**: senior
**Job type**: full-time
**Salary**: $155,584-$320,320 USD
**Category**: Engineering
**Industry**: Technology

**Apply**: https://job-boards.greenhouse.io/pinterest/jobs/7999050?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_9f5988ae-5b6

## Description

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.

Responsibilities:

- 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

Requirements:

- 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

## Skills

### Required
- Python
- Machine Learning
- Statistics
- Adtech
- CTV
- Real-time Bidding
- Programmatic Advertising

### Nice to have
- Cursor
- Copilot
- Codex
- LLM-powered productivity tools
- Causal inference
- Big data experience with Scala and Spark
- Systems programming experience in Zig or similar
- Reinforcement learning or bandit algorithms in production
- Experience building agentic AI systems or LLM-powered workflows
- MLOps experience

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Source: [Apply at job-boards.greenhouse.io](https://job-boards.greenhouse.io/pinterest/jobs/7999050?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)