# Sr. Software Engineer, tvScientific

**Company**: tvScientific
**Location**: San Francisco, CA, US; Remote, US
**Work arrangement**: remote
**Experience**: senior
**Job type**: full-time
**Salary**: $155,584-$320,320 USD",   "salaryMin": 155584,   "salaryMax": 320320,   "salaryCurrency": "USD",   "salaryPeriod": "year
**Category**: Engineering
**Industry**: Technology

**Apply**: https://job-boards.greenhouse.io/pinterest/jobs/7782563
**Canonical**: https://yubhub.co/jobs/job_d05f8013-902

## Description

We're seeking a Sr. Software Engineer to build out our simulation and AI capabilities. You'll design and implement systems that model the CTV advertising ecosystem , auction dynamics, bidding strategies, campaign outcomes, and counterfactual scenarios , and develop AI-driven tools that accelerate how we build, test, and deploy ML systems.

Key responsibilities include:

- 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

Requirements include:

- 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 include:

- 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

## Skills

### Required
- Systems programming
- Probabilistic modeling
- Stochastic processes
- Agent-based simulation
- Adtech experience
- Modern AI tools
- Clear written communication
- Ownership
- High integrity and ownership

### Nice to have
- Strong production Python skills
- Causal inference
- Discrete event simulation
- Reinforcement learning
- Big data experience
- MLOps experience