Why This Job is Featured on The SaaS Jobs
### Why this Role is Featured on The SaaS Jobs
While OpenAI is best known for frontier AI research, this role sits in the kind of platform engineering layer that increasingly underpins modern SaaS: dependable, reusable infrastructure that lets product and research teams ship without being blocked by data plumbing. Owning dataset reads for large-scale training runs highlights a SaaS-adjacent reality where internal platforms function like products, with defined APIs, reliability guarantees, and clear operational contracts.
For SaaS professionals, the career value here is in building systems that must be observable, reproducible, and resilient under sustained load. The work touches performance engineering, storage interfaces, developer experience, and incident-proofing, which are durable skills across data-heavy SaaS companies. Leading through code while aligning multiple infrastructure stakeholders mirrors the cross-team coordination expected in mature SaaS environments, where platform choices set long-term constraints for many downstream teams.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
About the Team
Training Runtime builds the distributed systems that power OpenAI's largest model training runs - most recently GPT-5.5! The Data Movement area owns the infrastructure that keeps training jobs supplied with the right data at the right time, and keeps model state moving safely and efficiently across large clusters.
Our work spans machine learning systems, distributed storage, high-throughput data loading, reliability engineering, and developer experience. Success means researchers can move quickly while training runs remain fast, reproducible, debuggable, and resilient at scale.
About the Role
We are looking for a deeply hands-on Technical Lead Manager to own datasets throughout our training infrastructure. This person will set the direction for how training jobs read data: the APIs, storage contracts, versioning model, benchmarks, debugging tools, and reliability guarantees that make data access consistent across current and future training frameworks.
You will begin as the primary technical owner for dataset reads, working directly in the code while aligning researchers, training framework owners, storage teams, and infrastructure partners around a durable platform. The problem is deceptively hard at frontier scale: make enormous, heterogeneous datasets easy to consume, fast to restart, correct across distributed workers, observable when something goes wrong, and flexible enough to support pretraining, reinforcement learning, and multimodal training.
In this role, you will
Design and build a unified dataset read platform for multiple current and future training frameworks.
Define dataset APIs, storage-format expectations, registration/versioning, and migration paths that make data access reproducible and maintainable.
Build reliability into the read path, including stateful iteration, caching, fast restart, recovery, and clear operational contracts.
Build terminal and web-based visualizers that let teams inspect text, multimodal, and reinforcement learning data late in the pipeline, where bugs are most visible.
Write and review production code in core data loading, service, caching, and reliability paths.
Partner with teams working on training frameworks, reinforcement learning, multimodal models, storage, runtime, and cluster infrastructure.
Over Time
The long-term goal is a team that owns fast, correct, scalable, and reliable in-cluster data movement for training: data that comes in, data that goes out, and data that moves around inside the cluster. After ramping on datasets, this role will expand to TLM ownership for broader data movement systems, including checkpoint loads/saves and snapshot transfers, while partnering closely with existing technical leads and adjacent infrastructure teams.
You might thrive in this role if you:
Have built or owned dataset, data loading, storage, or distributed training infrastructure at large scale (e.g. torch.utils.data)
Care equally about API design, debugging ergonomics, performance, and bit-level correctness.
Understand the failure modes of large distributed training jobs and know how data systems can create or prevent them.
Have experience with stateful iterators, checkpoint/restart semantics, caching, remote services, or high-throughput storage reads.
Are comfortable working across Python and lower-level systems code; Rust or C++ experience is useful but not required.
Have worked with multimodal, video, reinforcement learning, or pretraining data pipelines where small data bugs are expensive and hard to diagnose.
Can lead through code and technical judgment before a team exists, and can later manage engineers without losing the hands-on edge.
Obsess over developer experience by eliminating friction, such as manual preprocessing scripts and niche cluster-specific bugs, ensuring a reliable and efficient experience for researchers.
About OpenAI
OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.
For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.
Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.
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