Software Engineer, Infrastructure

The MRS 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 e2e 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.

Software Engineer, Infrastructure Responsibilities:

• Identify performance opportunities and bottlenecks across a wide range of MRS 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.

Minimum Qualifications:

• 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

Preferred Qualifications:

• Hands-on experience with large-scale AI infra systems (for example, GPU training clusters)
• Experience in training and/or inference solutions for large models (e.g. recommendation models or LLMs).
• Experience in high performance computing including communication optimization, CUDA kernel optimization, distributed training and inference, etc.

About Meta:

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.

Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about benefits at Meta.