Research Engineer, Monetization AI

Meta

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

Qualifications

  • Bachelor's degree in Computer Science or equivalent experience
  • 2+ years in research or applied research positions
  • Experience in deep learning, NLP, or related fields
  • Proficient in Python with PyTorch experience
  • Eligibility for work authorization in the hiring country

Responsibilities

  • Develop large-scale AI models using scaling and transfer learning
  • Optimize model performance through training and signal scaling
  • Drive advancements in NLP with NextGen sequence learning techniques
  • Design generative models for data augmentation
  • Research graph-aware language models
  • Deploy AutoML pipelines for efficiency
  • Apply Reinforcement Learning techniques for long-term optimization
  • Utilize causal learning to assess data relationships
  • Collaborate to enhance ML systems with efficient AI designs
  • Innovate solutions for data challenges using advanced AI techniques

Benefits

  • Opportunity to impact Meta’s monetization strategy and revenue
  • Work alongside industry-leading experts in AI and ML
  • Access to cutting-edge research and development resources
  • Collaborative environment with cross-functional teams
  • Focus on responsible and ethical AI practices
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 SOTA 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 NextGen 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.
• Experience working and communicating cross functionally in a team environment.
• First author publications at peer-reviewed AI conferences (e.g., NeurIPS, CVPR, ICML, ICLR, ICCV, ACL).
• Master's degree or PhD in Computer Science, Computer Engineering, Artificial Intelligence, Machine Learning, or relevant technical field.
• Experience taking ideas from research to production.
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies

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