Machine Learning Engineer

We’re building the ad layer for the AI entertainment era. Interactive brand experiences are embedded natively across the next generation of consumer apps, games, and interactive platforms. A bit of context on how the team thinks about itself: the market right now is chasing LLMs and AI agents. We’re not that. This is an interactive entertainment infrastructure built on traditional ML with a twist. Recommendation systems used to be the hottest seat in tech (Google Ads, Instagram Ads in the mid-2010s), and they're now somewhat out of fashion as the market chases AI agents. The team is looking for engineers who want depth on that real ML work rather than the AI agent hype cycle.

About the Role

We’re hiring an ML engineer to own the recommendation engine that decides, in real time, which ad reaches which user at which moment across millions of daily interactions and tens of millions in annualized ad spend. This is a full-stack ML role, you'll go from data pipelines to model architecture to production serving, with direct business impact at every layer. What You'll Build

Recommendation engine: Design and ship a low-latency ad ranking system (retrieval ranking reranking)

that selects the optimal campaign and creative for each ad opportunity, balancing advertiser ROAS against user experience

ML training infrastructure: Architect the data pipelines and feature stores that power continuous model training across reward signals

User and context modeling: Build representations of user behavior from conversational data, engagement history, and contextual signals (geo, device, session context, characters interacted with)

Serving infrastructure: Build the stack for sub-second latency and cost efficiency, given tight per-impression unit economics

Requirements (Must Have)

0-6 years of ML engineering experience. Cracked new grads welcome.

You've shipped a 1 ML system in production. Not just research or notebooks.

Backend depth across data architecture, feature pipelines, and serving infrastructure end to end

Hybrid infrastructure ML background

Zero-defect mindset and meticulous attention to latency, scalability, and reliability

Comfort with ambiguity. Some interesting open problems (delayed rewards, fatigue modeling, cold start). 

Bias toward shipping. Early-stage pace. Not a 9-to-5.

Based in SF or willing to relocate quickly. In-person preferred.

Nice-to-Have

Recommendation systems, ranking, or ad experience at scale PyTorch fluency

AdTech experience (plus, not a requirement)

Curiosity about AI-native products and interactive entertainment