Software Engineer, Machine Learning, Applied AI

Meta is seeking exceptional engineers to help deliver on one of the most ambitious goals in technology: personal superintelligence for everyone. As an engineer in the Applied AI Engineering (AAI) organization, you will apply your deep domain expertise, engineering skills, and sound judgment to make our frontier LLMs best-in-class on real-world tasks. You will identify gaps in current model capabilities and build the complex systems, agentic pipelines, reinforcement learning environments, and evaluations that generate high-quality training signals. Working closely with Meta Superintelligence Labs, AAI contributes to advancing Meta's frontier models and AI products.

We deliberately operate like an early-stage company: a flat, AI-native organization with light processes, wide scope, and fast decisions. We are looking for engineers with strong technical foundations and AI fluency who are energized by ambiguity and by advancing Meta's frontier AI capabilities.

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

8+ years of programming experience in a relevant language OR 4+ years experience with a PhD

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

Experience utilizing data and analysis to explain technical problems and provide detailed feedback and solutions Masters degree or PhD in Computer Science or a related technical field

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

Demonstrated ability to solve ambiguous problems, and propose process and technical improvements in a dynamic, fast moving organization

Experience designing automated pipelines that generate synthetic training data or evaluation tasks

Experience building verifiable environments and quality controls for AI training data, including test harnesses, automated graders, contamination detection and taxonomy coverage