About the Company (Stealth Mode)

This company is building the advertising layer for the next era of AI-driven entertainment. The focus is on enabling interactive brand experiences that are natively integrated into modern consumer apps, games, and other interactive digital platforms.

While much of the current market attention is on large language models and AI agents, this team is deliberately focused elsewhere. Their core work sits in interactive entertainment infrastructure powered by traditional machine learning with a strong emphasis on recommender systems—an area that once dominated tech (think large-scale ad ranking systems in major platforms) and is now being re-explored with a more focused, systems-heavy approach.

The company is backed by top-tier investors in venture capital and ad/gaming ecosystems and has already secured early revenue commitments. The team is very small today, and this role represents the first dedicated machine learning hire.

About the Role

They are looking for a Machine Learning Engineer to own and evolve a real-time recommendation system that determines which advertisement is shown to which user at each moment across a high-volume system with significant ad spend.

This is a full-stack ML role spanning data pipelines, model design, and production serving. The work is directly tied to revenue and user experience, with end-to-end ownership.

What You’ll Work On

Recommendation systems

• Design and implement a low-latency ad ranking pipeline (retrieval, ranking, reranking)
• Optimize for both advertiser performance (ROI/ROAS) and user experience

ML infrastructure

• Build and maintain data pipelines and feature stores for continuous model training
• Work with reward signals and feedback loops from production systems

User/context modeling

• Develop representations of user behavior using interaction history, conversational signals, and contextual data (device, location, session context, engagement patterns)

Serving systems

• Build and optimize inference infrastructure for sub-second latency and cost efficiency under tight per-impression economics

Requirements

Must Have

• 0–6 years of ML engineering experience (strong new grads considered)
• At least one production ML system shipped (not just research or notebooks)
• Strong backend and systems skills (data pipelines, feature engineering, serving)
• End-to-end understanding of ML systems in production environments
• High standards for reliability, latency, and scalability
• Comfortable working with ambiguity and open-ended problems (e.g., delayed rewards, cold start, fatigue effects)
• Strong bias toward shipping and working in fast-paced environments
• Based in San Francisco or willing to relocate quickly (in-person preferred)

Nice to Have

• Experience with recommendation systems, ranking systems, or ad platforms at scale
• Familiarity with PyTorch
• Prior AdTech experience
• Interest in AI-native products and interactive entertainment