Software Engineer, Systems — AI Training Data Infrastructure

We own the dataset layer that Meta's largest AI training runs read from. AIRStore and the Anywhere Training substrate are what let a multi-petabyte dataset be written once and read at full throughput from any cluster, in any region, in any cloud — without a copy. Our customers are named model programs, not abstract services: when a training job's GPUs go idle waiting on I/O, or a dataset isn't where the scheduler put the job, that is our problem to own and fix. In 2026 this team moved Anywhere Training blob pointers to 100% Manifold residency, cut AIRStore dataset startup time by 10×, drove the migration of AIRStore datasets onto a standard S3 interface, and reclaimed hundreds of petabytes through lifecycle work — all while holding the line on training reliability across dozens of production incidents.

Responsibilities

• Own significant components of the AIRStore data path end to end — ingestion, metadata, client, and read path — from design through production operation
• Attack throughput and latency as a first-class product concern: prefetching, parallelism, caching, and startup cost, measured in GPU utilization and training wall-clock rather than microbenchmarks
• Build the multi-region and multi-cloud story: make dataset location invisible to the training job, across Meta data centers and third-party clouds
• Get dataset lifecycle right — TTL, archival, expiration, and deletion — where the correctness bar is absolute in both directions: nothing a live run needs may disappear, and nothing that must be deleted may persist
• Own a widely embedded client library responsibly: compatibility, rollout safety, and blast-radius control across thousands of callers you don't control
• Take real operational ownership. Join the oncall rotation, drive root-cause analysis on incidents affecting production model training, and convert each one into a structural fix rather than a mitigation
• Partner directly with AI research and training teams, Manifold, Warm Storage, Privacy, and Crypto to land changes that cross system boundaries

Minimum Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• 5+ years of experience building and operating production distributed systems or large-scale data infrastructure
• Proficiency in a systems language — C++, Rust, or Go — plus Python
• Demonstrated ability to diagnose performance problems in production: profiling, tracing, and reasoning about I/O, network, and concurrency behavior at scale
• Experience owning a service in production, including oncall, incident response, and postmortem follow-through
• Track record of designing and delivering a substantial system component with limited direction
• BS/MS in Computer Science or equivalent practical experience

Preferred Qualifications

• Experience with data lifecycle, retention, and privacy-driven deletion at scale
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Evidence of influencing technical direction beyond your immediate team
• Background in ML data pipelines — dataloading, checkpointing, dataset versioning, or throughput-bound training I/O
• Experience with storage systems: object/blob stores, distributed filesystems, caching and prefetch layers, or dataset/columnar formats
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Experience running infrastructure across multiple cloud providers or hybrid environments
• Familiarity with S3-compatible object storage APIs and the practical tradeoffs of compatibility layers

$154,003/year to $217,006/year + bonus + equity + benefits