Software Engineer, Machine Learning

Meta builds technologies that help people connect, find communities, and grow businesses. The Software Engineer, Machine Learning will build innovative application experiences, architect scalable systems, optimize performance, and provide technical leadership across complex engineering initiatives. The role also involves applying machine learning, AI, information retrieval, and responsible AI practices to develop impactful products.

Responsibilities:

• Collaborate with cross-functional teams (product, design, operations, infrastructure) to build innovative application experiences
• Implement custom user interfaces using latest programming techniques and technologies
• Analyze and optimize code for quality, efficiency, and performance, and provide feedback to peers during code reviews
• Set direction and goals for teams, lead major initiatives, provide technical guidance and mentorship to peers, and help onboard new team members
• Architect efficient and scalable systems that drive complex applications
• Identify and resolve performance and scalability issues, and drive large efforts to reduce technical debt
• Work on a variety of coding languages and technologies
• Establish ownership of components, features, or systems with expert end-to-end understanding

Requirements:

• Demonstrated experience driving change within an organization and leading complex technical projects
• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• 8+ years of programming experience in a relevant language OR 4+ years experience with a PhD
• Experience utilizing data and analysis to explain technical problems and provide detailed feedback and solutions
• Experience with frameworks like TensorFlow, PyTorch, or Scikit-learn
• Understanding of information retrieval concepts, such as indexing, querying, and ranking
• Demonstrated grasp of data structures and algorithms, including graph theory and optimization techniques
• Knowledge of NLP techniques, including text preprocessing, tokenization, and sentiment analysis
• 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