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.

Responsibilities

• 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

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

• 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)

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