Machine Learning Engineer

Job not on LinkedIn

🕒 February 27

🏄 California – Remote

🏄 California – Remote

💵 $123.7k - $254.7k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Machine Learning Engineer

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tvScientific

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2020

📱 Media

☁️ SaaS

🤝 B2B

Media • SaaS • B2B

tvScientific is a performance-focused Connected TV (CTV) advertising platform that uses AI-powered optimization to deliver measurable business outcomes like installs, sales, and traffic. The platform provides inventory access, targeting, creative management, measurement & attribution, and real-time reporting with a unified dashboard and transparent analytics. tvScientific positions itself as a pay-for-performance solution for consumer brands, offering guaranteed outcomes, brand safety controls, and integrations for scale.

📋 Description

• 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 • Build AI agents that explore strategy spaces, generate hypotheses, and automate experimentation within simulated environments • Use LLMs and generative AI to accelerate internal ML workflows • 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

• Strong production Python skills and experience building simulation or modeling systems • Deep understanding of probabilistic modeling, stochastic processes, or agent-based simulation • Hands-on experience with modern AI tools: LLMs, code generation, agentic workflows • Adtech experience: you understand auction theory, RTB mechanics, and the dynamics of programmatic advertising • Ability to translate business questions into rigorous simulation frameworks • Clear written communication: you'll need to define new technical directions • Ownership: scope, design, and ship systems end-to-end with minimal direction • Nice-to-Haves: Causal inference; discrete event simulation; reinforcement learning; MLOps experience.

🏖️ Benefits

• Equity • Health insurance • Flexible work arrangements