tvScientific

tvScientific offers a pioneering Connected TV advertising and marketing platform that integrates traditional television's impact with the effectiveness of digital advertising, specifically tailored for performance marketers.

Media

Consumer Discretionary

11-50 (30)

Founded 2020

$22M raised

6 open positions

Links

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Software Engineer

1 hour, 47 minutes ago

United States

Full-time

Lead

Software Engineer

Software Development

Apache Spark AWS C C++ LLM MLOps Python Reinforcement Learning Rust Scala Zig

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tvScientific

Media

11-50

Founded 2020

$22M raised

View All Jobs 6

Description

Design and build simulation environments that model CTV auction mechanics, inventory supply, and advertiser competition.
Develop counterfactual and what-if frameworks to evaluate 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 deployments by validating new bidding and optimization strategies before they reach live traffic.
Define the technical direction for simulation and AI infrastructure and mentor engineers on the team.
Translate business questions into rigorous simulation frameworks that can be used for analysis and decision-making.
Scope, design, and ship systems end-to-end with minimal direction.

Requirements

Systems programming experience in Zig or a similar language such as C, C++, or Rust.
Deep understanding of probabilistic modeling, stochastic processes, or agent-based simulation.
Hands-on experience with modern AI tools, including LLMs, code generation, and agentic workflows.
Adtech experience with understanding of RTB mechanics and programmatic advertising dynamics.
Ability to translate business questions into rigorous simulation frameworks.
Clear written communication skills for defining technical directions and aligning others.
Demonstrated ownership in scoping, designing, and shipping systems end-to-end with minimal direction.
Demonstrated ability to use AI to improve speed and quality in day-to-day workflow for relevant outputs.
Strong track record of critical evaluation and verification of AI-assisted work, including testing, source-checking, data validation, and peer review.
High integrity and accountability when handling sensitive data and final deliverables.
Strong production Python skills and experience building simulation or modeling systems are preferred.
Causal inference experience, such as uplift modeling, synthetic controls, difference-in-differences, or incrementality testing, is preferred.
Experience with discrete event simulation, Monte Carlo methods, or digital twins is preferred.
Reinforcement learning experience using simulated environments for policy learning and evaluation is preferred.
Experience building agentic AI systems or multi-agent simulations is preferred.
Big data experience with Scala and Spark is preferred.
MLOps experience with model deployment, monitoring, and pipeline orchestration on AWS is preferred.

Benefits

Base salary range of $123,696 to $254,667 USD for US-based applicants.
Eligible for equity.
Remote work indicated by #LI-REMOTE.
Relocation assistance is not available for this role.
Access to Pinterest culture and benefits, with details available on the company careers site.

Interested in this position?

Apply directly on the company website

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