Staff Machine Learning Engineer (Ads Creative Effectiveness)

Join Reddit's Ads Creative Effectiveness team as a Staff Machine Learning Engineer. In this role, you will architect and implement advanced generative workflows for ad creative generation, build end-to-end image and video editing capabilities, and fine-tune large vision-language models to understand winning ad aesthetics. You will also engineer robust filtering and safety systems, collaborate with various teams, and embed brand-safety filters and copyright checks. The position offers comprehensive health benefits, flexible vacation, family planning funds, paid volunteer time off, and workspace/home office benefits.

• Architect and implement state-of-the-art pipelines for ad creative generation, moving beyond vanilla prompting to build systems capable of product, font, logo, and brand asset preservation.
• Build editing capabilities allowing advertisers to smart-crop, inpaint, outpaint, resize, and style-transfer assets while maintaining strict brand guidelines.
• Adapt large vision-language models to understand "winning" ad aesthetics, building feedback loops where ad performance metrics are used to fine-tune the generation process.

• Deep ML Expertise: Hands-on experience with tree models, neural nets, ranking, and especially LLM/VLM fine-tuning & RLHF/RLPF for ads
• Quant & Experimentation Chops: Comfort with uplift metrics, variance, minimum detectable effects and experimentation setups for generative systems
• Ads / Marketplace Insight: Understanding auction dynamics and advertiser pain points to create tools that drive revenue and retention
• Startup Mindset: Bias to action, comfort with ambiguity, and scrappiness: ACE evolved from a startup acquisition and still moves fast
• Communication Superpowers: Able to distill complex ML trade-offs for executives, product managers, and non-technical stakeholders alike
• Prior work on ad creative tooling: Headline generators, image generators, pre-test models, editing flows for the advertiser
• Experience navigating brand-safety, privacy, or copyright frameworks in generative AI products