Machine Learning Engineer, Ads
$165,000–$230,000 year
HybridSan Francisco, California, United States
Job Summary
Build and improve ML systems powering advertising products, including ranking, recommendation, targeting, prediction, and optimization. Develop models that improve ad creative quality, relevance, personalization, and performance at scale by connecting generative models with real-world advertising performance signals. Apply prompt engineering and post-training techniques to fine-tune foundation models for specific creative and advertising objectives. Design experiments across creative generation and delivery to understand drivers of advertiser performance, then build production ML systems that operate reliably at significant scale. Work closely with Product, Research, Engineering, and GTM teams to turn advances in generative AI into usable products. This role sits at the intersection of large-scale machine learning, generative AI, and advertising systems, helping define an AI-native advertising platform from the ground up.
Required Qualifications
- Deep experience building machine learning systems for advertising
- Strong understanding of ads systems, including areas such as ranking, recommendation, targeting, bidding, conversion prediction, creative optimization, or measurement
- Hands-on experience with LLMs, multimodal models, or generative AI systems
- Strong experience with prompt engineering and model evaluation
- Experience with post-training, including techniques such as supervised fine-tuning, preference optimization, reinforcement learning, or related approaches
- Strong software engineering fundamentals and experience shipping production ML systems
- Ability to operate across research and engineering: you can experiment quickly, identify what works, and turn it into a scalable production system
- High agency
- Working English
- This is a hybrid role based in the San Francisco Bay Area
- Team members are expected to work from our San Francisco office three full days per week
- remaining days worked remotely
- expected to be available during agreed working hours
- maintain sufficient overlap with the relevant team's time zone
Desired Qualifications
- Experience building ads, ranking, or recommendation systems at a major consumer, social, search, or advertising platform
- Experience with generative video, image, or multimodal models
- Experience using downstream signals such as CTR, CVR, ROAS, engagement, or retention to train or optimize ML systems
- Experience with large-scale model training, inference optimization, or distributed ML infrastructure
- Experience building AI systems that generate or optimize advertising creative
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