Staff Machine Learning Engineer, Ads Creative Effectiveness
New
J
JobgetherAdvertising technology
Remote position available across the United States.Full-TimeStaff
SalaryBase salary range of $230,000–$322,000 USD
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Job Details
- Required Skills
- Machine Learning
Requirements
- Bring deep hands-on machine learning expertise spanning tree-based models, neural networks, and ranking systems.
- Have expertise in LLM/VLM fine-tuning and reinforcement learning approaches such as RLHF or RLPF for advertising applications.
- Demonstrate quantitative and experimentation skills, including familiarity with uplift metrics, variance, minimum detectable effects, experimental design, and evaluation frameworks for generative systems.
- Understand advertising or marketplace dynamics, including auction mechanisms, advertiser challenges, revenue drivers, and factors influencing campaign performance and retention.
- Have experience developing or leading ad creative technologies, such as headline generators, image generators, creative pre-test models, or advertiser-focused editing workflows.
- Apply strong product and business judgment and turn complex technical challenges into practical solutions in ambiguous, fast-moving environments.
- Explain machine learning concepts, trade-offs, risks, and opportunities to executives, product leaders, and non-technical stakeholders.
- Experience with brand safety, privacy, copyright, or compliance considerations in generative AI products is desirable.
Responsibilities
- Architect and implement generative workflows for advertising creative that preserve products, fonts, logos, and other brand assets.
- Build image and video editing capabilities, including smart cropping, inpainting, outpainting, resizing, and style transfer.
- Fine-tune large language and vision-language models to understand high-performing advertising aesthetics and creative patterns.
- Connect ad performance metrics such as CTR and CVR to generation quality through feedback loops.
- Explore reinforcement learning approaches such as RLHF and RLPF using performance and user feedback.
- Design safety, compliance, brand-protection, and copyright safeguards, including filtering and human-in-the-loop escalation.
- Partner with Product, Sales, Policy, UX, and Ads ML teams to integrate capabilities into advertiser-facing tools and the broader ad delivery experience.
- Use experimentation and quantitative methods to evaluate generative systems and optimize creative performance.
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