Technical Lead Manager - Training Runtime, Data(set) Movement
WFA Digital Insight
With the explosive growth of AI research, demand for technical leaders in data movement and reliability has never been higher. According to industry reports, the need for skilled professionals in this space grew by 28% in 2025 alone. Openai, a pioneer in AI research, is now seeking a Technical Lead Manager to spearhead its training runtime and dataset movement efforts. As a hub for innovation, Openai offers a unique opportunity for tech professionals to make a significant impact. Before applying, candidates should be aware that this role requires a deep understanding of distributed systems, data loading, and reliability engineering, as well as experience in leading technical teams and managing complex data pipelines.
Job Description
About the Role
The Technical Lead Manager position at Openai is a critical role that involves overseeing the development and maintenance of the company's training runtime and dataset movement infrastructure. As a key member of the Training Runtime team, the successful candidate will be responsible for designing and building a unified dataset read platform for multiple current and future training frameworks. This role requires a deep understanding of distributed systems, data loading, and reliability engineering, as well as experience in leading technical teams and managing complex data pipelines.The Training Runtime team is responsible for building the distributed systems that power Openai's largest model training runs. The Data Movement area, in particular, focuses on 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. The team's work spans machine learning systems, distributed storage, high-throughput data loading, reliability engineering, and developer experience.
What You Will Do
- 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
- Lead the development of a durable platform that supports pretraining, reinforcement learning, and multimodal training
- Collaborate with researchers, training framework owners, storage teams, and infrastructure partners to align around a unified platform
- Identify and mitigate potential failure modes of large distributed training jobs and develop strategies to prevent them
What We Are Looking For
- Experience building or owning dataset, data loading, storage, or distributed training infrastructure at large scale
- Strong understanding of API design, debugging ergonomics, performance, and bit-level correctness
- Knowledge of the failure modes of large distributed training jobs and how data systems can create or prevent them
- Experience with stateful iterators, checkpoint/restart semantics, caching, remote services, or high-throughput storage reads
- Comfort working across Python and lower-level systems code, with Rust or C++ experience being a plus
- Experience working with multimodal, video, reinforcement learning, or pretraining data pipelines
- Ability to lead through code and technical judgment, with a focus on eliminating friction and ensuring a reliable and efficient experience for researchers
Nice to Have
- Experience with machine learning frameworks and technologies, such as TensorFlow or PyTorch
- Knowledge of distributed systems and cloud computing platforms, such as AWS or GCP
- Familiarity with data visualization tools and technologies, such as Tableau or D3.js
- Experience with Agile development methodologies and version control systems, such as Git
Benefits and Perks
- Competitive salary and equity package
- Opportunity to work on cutting-edge AI research and development projects
- Collaborative and dynamic work environment with a team of experienced professionals
- Flexible working hours and remote work options
- Access to professional development and training opportunities
- Comprehensive health and wellness benefits package
- Generous paid time off and vacation policy
- Remote work stipend and equipment allowance
- Opportunity to contribute to open-source projects and participate in industry conferences and events
How to Stand Out
- Develop a strong understanding of distributed systems, data loading, and reliability engineering to stand out in the application process
- Create a portfolio that showcases your experience with dataset, data loading, storage, or distributed training infrastructure at large scale
- Be prepared to discuss your experience with stateful iterators, checkpoint/restart semantics, caching, remote services, or high-throughput storage reads during the interview process
- Emphasize your ability to lead through code and technical judgment, with a focus on eliminating friction and ensuring a reliable and efficient experience for researchers
- Research Openai's current projects and initiatives to demonstrate your passion for AI research and development
- Practice whiteboarding exercises to improve your problem-solving skills and ability to communicate complex technical concepts
- Be prepared to negotiate salary and benefits, and do not be afraid to ask about opportunities for professional development and growth
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