Machine Learning Engineer, Ads

Responsibilities

• Build and improve ML systems for advertising ranking, recommendation, targeting, prediction, and optimization.
• Develop models that improve ad creative quality, relevance, personalization, and performance at scale.
• Connect generative models with advertising performance signals to create continuously improving feedback loops.
• Apply prompt engineering, post-training, fine-tuning, preference optimization, and evaluation techniques to generative models.
• Design and run experiments across creative generation, ranking, targeting, and delivery.
• Build reliable production ML systems spanning experimentation, inference, and serving.
• Collaborate with Product, Research, Engineering, and go-to-market teams to turn generative AI advances into advertiser-facing products.

Requirements

• Deep experience building machine learning systems for advertising.
• Strong understanding of ads systems, including 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 techniques such as supervised fine-tuning, preference optimization, or reinforcement learning.
• Strong software engineering fundamentals and experience shipping production ML systems.
• Ability to move between research experimentation and scalable production engineering.
• Working English and high agency.
• Preferred experience with ads, ranking, or recommendation systems at a major consumer, social, search, or advertising platform.
• Preferred experience with generative video, image, or multimodal models; downstream signals such as CTR, CVR, ROAS, engagement, or retention; large-scale training; inference optimization; distributed ML infrastructure; or AI systems for advertising creative.

Benefits

• Base salary range of $165,000–$230,000, depending on experience, skills, scope, and location.
• Eligibility to participate in the company stock option program.
• Comprehensive benefits package.
• Opportunity to work on ambitious AI products with an experienced international team.
• Significant ownership, direct impact, and professional growth opportunities.
• Hybrid work in the San Francisco Bay Area with three full days per week in the San Francisco office and remaining days remote.
• Expected availability during agreed working hours and sufficient overlap with the relevant team’s time zone.