Research Engineer, Monetization AI

Meta

$130K — $180K *
Consumer Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • Bachelor's degree in Computer Science, Computer Engineering, or related field
  • 2+ years of experience in industry or research roles
  • Research experience in deep learning, RL, NLP, computer vision, or related fields
  • Proficient in Python and frameworks like PyTorch
  • Work authorization in the employment country required

Responsibilities

  • Develop large-scale model architectures using scaling and transfer learning
  • Optimize model performance through training and signal scalability
  • Advance NLP with cutting-edge sequence learning techniques
  • Design generative models for data augmentation
  • Research graph-aware large language models
  • Deploy AutoML pipelines effectively
  • Implement Reinforcement Learning techniques for value optimization
  • Collaborate cross-functionally to enhance ML systems with hardware-software co-design

Benefits

  • Opportunity to work with cutting-edge AI/ML technologies
  • Impact on Meta's products and long-term business goals
  • Contribute to revenue generation with significant financial implications
  • Access to research and development resources in a pioneering AI environment
  • Collaborative team atmosphere with cross-functional engagement
Full Job Description
We are the Monetization Ranking AI Research organization, dedicated to delivering personalized ads that maximize both user utility and advertiser value. We focus on advancing AI and ML technologies for all aspects of Monetization, including ranking, retrieval, model architecture, and optimization. By consistently integrating cutting-edge AI/ML advancements, we help Meta's products achieve long-term goals and have contributed tens of billions in revenue. With our growing impact, we're seeking motivated AI specialists to join our team and drive state-of-the-art research across the Monetization organization.

Responsibilities

Develop and implement large-scale model architectures, leveraging model scaling and transfer learning techniques
• Prioritize training scalability and signal scaling to optimize model performance, efficiency, and reliability
• Develop and apply next-generation sequence learning techniques to drive advancements in natural language processing and understanding
• Design and implement generative modeling solutions for data augmentation
• Research and develop graph-aware large language models
• Develop and deploy AutoML pipelines
• Apply Reinforcement Learning (RL) techniques, including long-term value optimization, RLHF, and RL4Reason
• Use causal learning to identify and understand the cause and effect of relationships across data
• Collaborate with cross-functional teams to design and optimize ML systems, leveraging expertise in hardware-software co-design, including quantization, compression, and resource-efficient AI, to drive performance improvements and efficiency gains
• Develop and implement innovative solutions for data-related challenges, utilizing knowledge of semi/self-supervised learning, generative techniques, sampling, debiasing, domain adaptation, continual learning, data augmentation, cold-start, content understanding, and large language models

Minimum Qualifications
• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• 2+ years of experience holding an industry, faculty, or government researcher or applied researcher position, or related position
• Research experience in deep learning, reinforcement learning, natural language processing, computer vision, recommendations, ranking, search, or related areas
• Programming experience in Python and hands-on experience with frameworks such as PyTorch
• Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment

Preferred Qualifications
• Experience solving complex problems and comparing alternative solutions, tradeoffs, and different perspectives to determine a path forward
• First author publications at peer-reviewed AI conferences (e.g., NeurIPS, CVPR, ICML, ICLR, ICCV, ACL)
• Experience working and communicating cross-functionally in a team environment
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Experience taking ideas from research to production
• Master's degree or PhD in Computer Science, Computer Engineering, Artificial Intelligence, Machine Learning, or relevant technical field
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)

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