Currently tracking 199 active AI roles, down 29% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $230k–$555k (avg $372k).
OpenAI currently has 235 active AI-related job listings. The majority of these roles are in the application stage, accounting for 32% of the total, followed closely by the agents stage at 29%. The dominant function for hiring is Engineering, with 168 positions. Frequent tech tags include model_serving, evals, and agent_orchestration, suggesting a focus on deploying and managing AI models. In the last 30 days, OpenAI posted 50 new AI roles, representing a 14% increase compared to the previous 30-day period.
OpenAI currently has 254 active AI-related roles in our index. The most common open titles are: AI Deployment Engineer (4), Partner AI Deployment Engineer - AWS (4), AI Deployment Engineer - Startups (3), AI Deployment Engineer- Codex (3), AI Deployment Engineer, Startups (2). Most positions are in Engineering and Research.
OpenAI's active AI hiring is concentrated in: application (33%), agents (28%), serving infrastructure (10%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
OpenAI is hiring AI talent in: United States (203 roles), United Kingdom (14 roles), Japan (6 roles), Germany (5 roles).
Job postings at OpenAI most frequently reference: model serving, agent orchestration, evals, llm observability, inference infra.
In the past 30 days, OpenAI has posted 56 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| Technical Lead Manager - Training Runtime, Data(set) Movement Technical Lead Manager for Training Runtime, focusing on the Data Movement area. This role owns the infrastructure for supplying training jobs with data and managing model state during large-scale model training runs. It involves designing and building a unified dataset read platform, defining APIs, storage contracts, and ensuring reliability and reproducibility. | Data | 9 |
| Research Infrastructure Engineer, Training Systems This role is for a Research Infrastructure Engineer focused on ML training systems at OpenAI. The engineer will build and maintain the infrastructure that enables novel research ideas for large-scale model training, improving reliability, debuggability, and performance. The work involves debugging across various systems (Python, PyTorch, distributed systems, GPUs, networking, storage) and designing APIs for complex training workflows. | Data |
| 9 |
| Machine Learning Engineer, Distributed Data Systems - Robotics Machine Learning Engineer focused on designing and scaling distributed data infrastructure for large-scale multimodal training and evaluation in robotics, ensuring reliability and efficiency for rapid iteration cycles. | DataEval Gate | 9 |
| Software Engineer, Platform Systems Software Engineer on the Platform Systems team at OpenAI, responsible for designing and building distributed systems for failure detection, tracing, and observability in large-scale AI training jobs. This role focuses on operating and improving the reliability and performance of OpenAI's training infrastructure. | DataServe | 9 |
| Software Engineer, Distributed Data Systems - Robotics Software Engineer to design and scale infrastructure for large-scale multimodal training and evaluation in robotics at OpenAI. Focus on distributed data pipelines, ML infrastructure, and ensuring scalability and reliability. | DataEval Gate | 9 |
| Training: ML Framework Engineer This role focuses on improving the core distributed machine-learning training runtime for OpenAI, aiming to accelerate researchers and enable frontier-scale model runs. The engineer will work on high-performance data movement, fault-tolerant training frameworks, and distributed process management to increase both training throughput and researcher throughput. | DataPretrain | 9 |
| Training Performance Engineer The Training Performance Engineer will drive efficiency improvements across OpenAI's distributed training stack, focusing on optimizing GPU utilization, throughput, and uptime for large-scale distributed model training. This role involves profiling, analyzing performance bottlenecks in compute, communication, and storage, and collaborating with engineers to improve kernel efficiency, scheduling, and collective communication performance, particularly for pre-training. | Data | 9 |
| Software Engineer, Collective Communication Software Engineer focused on the collective communication stack for large-scale AI model training, using C++ and CUDA to optimize network performance on custom supercomputers. This role directly supports the training of OpenAI's flagship models and collaborates with ML researchers. | Data | 9 |
| Software Engineer, ML Systems & Training Architecture Software Engineer focused on ML Systems & Training Infrastructure for the OpenAI Robotics team, responsible for maintaining and improving the training framework, debugging ML systems, and unblocking researchers and engineers. | Data | 8 |
| Training, Process Management Engineer OpenAI's Training Runtime team is seeking a Process Management Engineer to build and maintain the distributed OS responsible for launching, coordinating, and supervising large-scale machine learning training workloads. This role involves working with Rust and Python to create high-performance, reliable, and observable systems that enable researchers to scale ideas from experiments to production training runs on massive clusters. | Data | 8 |
| Software Engineer, Platform Systems Software Engineer on the Platform Systems team at OpenAI, responsible for designing and building distributed systems for failure detection, tracing, and observability to support large-scale AI model training. The role focuses on improving the reliability, performance, and visibility of OpenAI's training infrastructure. | Data | 8 |
| Software Engineer, Research - Human Data Software Engineer, Research - Human Data role at OpenAI. Focuses on building and maintaining full-stack systems for feedback collection, data labeling, and evaluation pipelines to train and improve AI models. Partners with researchers to translate alignment research into scalable production infrastructure, including inference and model training stacks. Designs user-facing tools and backend services for high-quality data workflows, driving infrastructure improvements for OpenAI's frontier models. | DataPost-train | 8 |
| Technical Lead Manager, Data Engineering, Trust & Safety Technical Lead Manager for Trust & Safety Data Engineering team at OpenAI. This role involves leading a team, setting strategy, shaping data architecture, and driving execution on high-impact data systems for fraud and abuse detection, safety measurement, and ML feature generation. The focus is on building privacy-safe datasets and pipelines to support trust and safety initiatives. | DataPost-train | 7 |
| Technical Program Manager, Human Data This role focuses on designing and executing data collection campaigns to support AI research and model development, particularly for LLMs. The TPM will collaborate with researchers and engineers to define data needs, build and optimize data platforms, and ensure data quality for training and evaluation purposes. | Data | 7 |
| Research Engineer, 3D & Multi-View Geometry Research Engineer focused on 3D and multi-view geometry for robotics, developing systems for perception, localization, and reconstruction from multi-camera sensor data. Integrates classical geometry with modern ML for real-time and large-scale pipelines. | DataServe | 7 |
| Software Engineer, Productivity - Training Runtime Software Engineer focused on improving the developer experience and productivity within OpenAI's core training and inference frameworks. This role involves optimizing iteration speed, CI, and testing strategies to support researchers working on frontier experiments. | DataServe | 7 |
| Simulation Environments Engineer The role focuses on building tooling and infrastructure for creating high-coverage, realistic virtual environments for robotics research and evaluation. This involves designing content pipelines, authoring procedural generators, and shipping tools for environment creation, quality control, and integration with simulation farms and ML evaluation pipelines. The role sits at the intersection of game-engine practice, asset engineering, and large-scale simulation infrastructure. | DataEval Gate | 7 |