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# ML Engineer - Simula

- **Company:** David Joseph & Company
- **Location:** San Francisco, CA, United States
- **Salary:** $160,000.0 - $220,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Architecture, Machine Learning, Recommender Systems, Pytorch, Backend, Low Latency, Data Pipelines
- **Published:** May 27, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=e1a8d8d0fb7a62e0

## About the Role

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

## 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


## Related Videos

- [How We Built a Machine Learning-Based Recommendation System (And Survived to Tell the Tale)](https://www.wearedevelopers.com/videos/752-how-we-built-a-machine-learning-based-recommendation-system-and-survived-to-tell-the-tale)
- [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding)
- [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock)
- [Swapping Low Latency Data Storage Under High Load](https://www.wearedevelopers.com/videos/746-swapping-low-latency-data-storage-under-high-load)
- [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail)
- [Serverless deployment of (large) NLP models ](https://www.wearedevelopers.com/videos/158-serverless-deployment-of-large-nlp-models)

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