ML Engineer - Simula
Role details
Job location
Tech stack
Job description
Simula is 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: from data pipelines to model architecture to production serving, with direct business impact at every layer., * 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
Do you have experience in System design?, * 0-6 years of ML engineering experience - strong new grads welcome
- Shipped at least one 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 with meticulous attention to latency, scalability, and reliability
- Comfort with ambiguity - delayed rewards, fatigue modeling, cold start are open problems here
- Bias toward shipping and early-stage pace
- Based in SF or willing to relocate quickly - in-person preferred, * Recommendation systems, ranking, or ad experience at scale
- PyTorch fluency
- AdTech experience
- Curiosity about AI-native products and interactive entertainment