Software Engineer Leadership, Machine Learning Recsys

Meta Meta · Big Tech · Sunnyvale, CA

Software Engineer Leadership role focused on Machine Learning Recommendation Systems for a consumer product at Meta. The role involves building and scaling ML models for recommendation systems, integrating LLMs, and ensuring responsible AI practices. Requires experience in developing ML models at scale, recommendation systems, and LLM applications, with a focus on shipping high-quality, reliable products.

What you'd actually do

  1. Collaborate with cross-functional teams (product, design, operations, infrastructure) to build innovative application experiences
  2. Implement custom user interfaces using latest programming techniques and technologies
  3. Develop reusable software components for interfacing with back-end platforms
  4. Analyze and optimize code for quality, efficiency, and performance
  5. Lead complex technical or product efforts and provide technical guidance to peers

Skills

Required

  • Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
  • 4+ years of experience in software engineering, or a relevant field.
  • 3+ years of experience in one or more of the following areas: machine learning, recommendation systems, artificial intelligence, or related technical field
  • Experience with developing machine learning models at scale from inception to business impact
  • Knowledge developing and debugging in C/C++ and Java, or experience with scripting languages such as Python, Perl, PHP, and/or shell scripts
  • Experience with scripting languages such as Python, Javascript or Hack
  • Proven experience designing, building, or deploying recommendation systems (e.g., collaborative filtering, content-based, hybrid approaches, personalization at scale)
  • Hands-on experience working with large language models (LLMs), such as BERT, GPT, or similar architectures, including fine-tuning, integration, or application in production environments
  • Experience building and shipping high quality work and achieving high reliability
  • Experienced in utilizing data and analysis to explain technical problems and providing detailed feedback and solutions
  • 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

Nice to have

  • 3+ years of experience if you have a PhD
  • PhD in Computer Science or a related technical field
  • Exposure to architectural patterns of large scale software applications
  • Experience with scripting languages such as Pytorch and TF
  • Publications in top-tier conferences/journals, patents, or open-source contributions in the recommendations or LLM space

What the JD emphasized

  • machine learning, recommendation systems, artificial intelligence, or related technical field
  • developing machine learning models at scale from inception to business impact
  • designing, building, or deploying recommendation systems
  • working with large language models (LLMs)
  • fine-tuning, integration, or application in production environments
  • building and shipping high quality work
  • achieving high reliability
  • integrating AI tools to optimize/redesign workflows
  • driving measurable impact
  • implementing responsible, ethical AI practices
  • demonstrated ongoing AI skill development
  • prompt/context engineering
  • agent orchestration
  • staying current with emerging AI technologies

Other signals

  • building cutting-edge products
  • improve existing products
  • advance the way people connect globally
  • implement custom user interfaces
  • develop reusable software components
  • analyze and optimize code
  • architect efficient and scalable systems
  • identify and resolve performance and scalability issues
  • establish ownership of components, features, or systems
  • machine learning, recommendation systems, artificial intelligence
  • developing machine learning models at scale from inception to business impact
  • designing, building, or deploying recommendation systems
  • working with large language models (LLMs)
  • fine-tuning, integration, or application in production environments
  • building and shipping high quality work
  • achieving high reliability
  • integrating AI tools to optimize/redesign workflows
  • driving measurable impact
  • implementing responsible, ethical AI practices
  • demonstrated ongoing AI skill development
  • prompt/context engineering
  • agent orchestration
  • staying current with emerging AI technologies