About the roleWe're looking for exceptional Machine Learning Engineers focused on Ads to help take Higgsfield's advertising platform to the next level.
You'll work at the intersection of large-scale machine learning, generative AI, and advertising systems-building the models and infrastructure that determine how creative is generated, ranked, optimized, and ultimately performs.
This role is for someone who understands ads systems deeply and is equally strong in modern generative AI. You should be comfortable moving across ranking and recommendation, targeting and optimization, prompt engineering, post-training, and production ML systems.
You'll help define what an AI-native advertising platform looks like from the ground up.
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- 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.
Nice to have- 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.
Compensation & Benefits• We offer a competitive and thoughtfully structured compensation package designed to align with impact, experience, and long-term growth.
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Base Salary: The anticipated salary range for this role is
$165 - $230k, depending on experience, skills, scope, and location.
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Equity: In addition to cash compensation, employees are eligible to participate in the company's stock option program and share in Higgsfield's long-term growth.
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Benefits: We offer a comprehensive benefits package designed to support employees professionally and personally
• The opportunity to work on ambitious AI products alongside a highly experienced, fast-moving, and international team.
• Significant ownership, direct impact, and opportunities for professional growth as the company scales.
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, with the remaining days worked remotely and are expected to be available during agreed working hours and to maintain sufficient overlap with the relevant team's time zone. We value in-person collaboration, active communication, responsiveness, and close partnership across teams.
