# Sr. Software Engineer, tvScientific

**Company:** [Pinterest](https://hotfix.jobs/companies/pinterest)
**Location:** Remote
**Role:** ML Engineering
**Salary:** $156k – $320k/yr
**Skills:** Zig, C, C++, Rust, Python, LLMs, Rtb, Scala, Spark, AWS, MLOps, Reinforcement Learning, Monte Carlo, Probabilistic Modeling
**Posted:** 2026-04-23

> Build simulation environments and AI agents to model CTV advertising auctions, bidding strategies, and counterfactual scenarios. Requires systems programming in Zig/C++/Rust, adtech knowledge, and AI tool expertise to de-risk ML deployments and mentor engineers.

## Job Description

## What you’ll do:
- Design and build simulation environments that model CTV auction mechanics, inventory supply, and advertiser competition
- Develop counterfactual and what-if frameworks for evaluating bidding strategies, budget allocation, and pacing algorithms offline
- Build AI agents that explore strategy spaces, generate hypotheses, and automate experimentation within simulated environments
- Use simulation to de-risk ML model deployments — validate new bidding and optimization strategies before they touch live traffic
- Define the technical direction for simulation and AI infrastructure and mentor engineers on the team

## What we’re looking for:
- Systems programming experience in **Zig** or similar (**C**, **C++**, **Rust**)
- Deep understanding of probabilistic modeling, stochastic processes, or agent-based simulation
- Hands-on experience with modern AI tools: **LLMs**, code generation, agentic workflows — and good judgment about when they help vs. when they don't
- Adtech experience: you understand **RTB** mechanics, and the dynamics of programmatic advertising
- Ability to translate business questions ("what happens if we change our bid strategy?") into rigorous simulation frameworks
- Clear written communication: you'll be defining new technical directions and need to bring others along
- Ownership: you scope, design, and ship systems end-to-end with minimal direction
- 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:
- Strong production **Python** skills and experience building simulation or modeling systems
- Causal inference — uplift modeling, synthetic controls, difference-in-differences, or incrementality testing
- Experience with discrete event simulation, **Monte Carlo** methods, or digital twins
- **Reinforcement learning** — using simulated environments for policy learning and evaluation
- Experience building agentic AI systems or multi-agent simulations
- Big data experience with **Scala** and **Spark**
- **MLOps** experience — model deployment, monitoring, and pipeline orchestration on **AWS**

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