Software Engineer, Infrastructure

The ML Infra team is focusing on ML Infra performance and efficiency for both large-scale AI training and inference workflows in the recommendation domain.

In this role, you will work on optimizing the end-to-end stack for model training and inference for large-scale recommendation models, with opportunities coming from the domains of distributed systems, model/system co-design, GPU optimizations, and more.

While the core of day-to-day work and key responsibility will be to identify and lead the execution for short/mid-term opportunities for efficiency optimization, you will also drive long-term strategies and shape team direction on things like model/system co-design, performance automation, regression detection and mitigation, etc.

• Identify performance opportunities and bottlenecks across a wide range of recommendation models, infrastructure and systems
• Implement changes to capture efficiency improvements
• Guide other engineers both inside and outside the team to execute on efficiency and performance opportunities, issues and bottlenecks
• Drive cross-functional collaborations and alignments with multiple partner or product ML teams
• Define technical direction(s), strategy and roadmap for the team
• Provide mentorship and guidance to grow other teammates

• BS/MS in Electrical Engineering, Computer Science or a related field or equivalent experience
• 5+ years of experience in AI Infra or System performance
• Hands-on experience in optimizing complex software solutions, such as distributed systems, large-scale CPU/GPU clusters, or similar
• Demonstrated experience in driving team execution and reaching alignment with cross-functional partners
• Previous experience in mentoring and growing software and/or machine-learning engineers as either a tech lead or a manager
• Capacity to investigate and debug issues in complex systems, including ones spanning multiple components or sub-systems

• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Experience in high performance computing including communication optimization, CUDA kernel optimization, distributed training and inference, etc
• Experience in training and/or inference solutions for large models (e.g. recommendation models or LLMs)
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Hands-on experience with large-scale AI infra systems (for example, GPU training clusters)

Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.

Skills

Collaborative Filtering, Software Debugging, Liaising with cross functional teams, Engineering Management, Agent Orchestration, Mentoring for Developers, Scalable Systems, Computer Science, Cross Functional Team Building, System Performance, mentors team members, Efficiency Improvement, Experience Design, roadmap strategies, integrated workflow, Context Engineering