Summary
What you’ll impact
Our company is seeking Machine Learning Engineers focused on Ads to build and scale AI-powered advertising systems. The role involves developing models, infrastructure, and feedback loops that improve ad creative quality, relevance, and performance, while collaborating across product, research, engineering, and go-to-market teams.
Responsibilities
What you'll do
- 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.
- Build systems that connect generative models with real-world advertising performance signals, creating feedback loops that continuously improve model outputs.
- Apply prompt engineering and post-training techniques to improve generative models for advertising and creative use cases.
- Work on fine-tuning, preference optimization, evaluation, and other techniques for adapting foundation models to specific creative and advertising objectives.
- Design and run experiments across creative generation, ranking, targeting, and delivery to understand what drives advertiser performance.
- Build production ML systems that operate reliably at significant scale, from experimentation through inference and serving.
- Work closely with Product, Research, Engineering, and GTM teams to turn advances in generative AI into products advertisers can use.
Requirements
What you’ll bring
- 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.