Researcher, Alignment Science
As a Research Engineer / Research Scientist on the Alignment team, you will design and implement alignment experiments focused on intent following, honesty, calibration, and robustness. You will train and evaluate models using reinforcement learning and other empirical machine learning methods. Your role includes developing evaluations for failure modes such as hallucination, instruction-following failures, reward hacking, covert actions, and scheming. You will study methods that encourage models to verify their behavior and report shortcomings honestly, including confession-style training objectives. You will build monitoring and inference-time interventions that ensure compliant behavior or surface model issues to users or downstream systems. Additionally, you will investigate how alignment methods scale with model capability, compute, data, context length, action length, and adversarial pressure. You will integrate successful techniques into model training and deployment workflows, produce externally publishable research when results advance the broader science of alignment, and collaborate with researchers and engineers across post-training, reinforcement learning, evaluations, safety, and product-facing teams.
Machine Learning Research, RF Foundation Models Specialist
Formulate new machine learning problems in RF sensing and spectrum understanding. Design experiments and evaluation approaches reflecting real operating conditions such as domain shift, changing interference, and varying sensors and platforms. Build models for structured, noisy, and partially observed signal environments. Improve robustness across propagation, interference, and low-visibility waveform conditions. Optimize models for throughput, latency, and deployment constraints. Move promising research into a release path for real systems through proofs-of-concept, realistic validation, and conversion into maintainable, deployable code. Use field performance to inform the development of the next generation of models and tooling. Work across the lifecycle of research and deployment including data and evaluation design, experimentation, model development, release readiness, and iteration based on real-world outcomes. Collaborate closely with embedded, hardware, and mission teammates, influencing how machine learning capability is built as the company scales.
Researcher, Agentic Post-Training
Own end-to-end research and engineering projects to improve the final post-training of OpenAI’s agentic models. Decide which integrations are ready for inclusion in major model runs in collaboration with partner teams. Develop horizontal model improvements in areas such as factuality, instruction following, tool/function calling, multi-agent behavior, and reasoning-effort calibration. Build and improve training, evaluation, grading, and data infrastructure for large-scale reinforcement learning/post-training runs. Create evaluations and diagnostics to assess model readiness for deployment. Enhance feedback loops from real product usage into post-training, including learning from implicit user feedback. Collaborate with Codex, API, ChatGPT, product, training, and other post-training teams to make frontier models more useful, reliable, and agentic.
Senior Research Engineer
As a Senior Research Engineer, you will lead research and engineering efforts to improve core conversational capabilities in production including instruction following, retrieval, memory, and long-horizon task completion. You will build and iterate on end-to-end models and pipelines that optimize for quality, efficiency, and user experience. You will partner with platform and product engineers to integrate new models into production systems. Additionally, you will break down ambiguous research ideas into clear, iterative milestones and roadmaps.
Graduation Internship - AI Research - Paris
Participate in research and development within the Models team, Data Research team, or Agent team in H Company's research lab as a graduation internship; work on building foundational models powering agentic technology, advancing multimodal intelligence through large-scale models, and defining new learning algorithms and agent paradigms for autonomous AI systems.
PhD Research Intern, Vision Language Action Models
Work on the Multimodal Language Action model by exploring novel discrete action tokenization and flow matching approaches, building on MotionLM, FAST, and other models. Train models at the billion+ scale using millions of miles of proprietary Zoox driving data. Gain experience and insight into training Multimodal Language Action models at scale. Contribute to publishable research that could be integrated into Zoox vehicles.
AI Research Director
The AI Research Director leads webAI's AI and ML research strategy including long-term vision, experimentation roadmap, and architectural innovation. They oversee research on large language models, diffusion and multimodal models, inference optimization, and distributed execution. The role advances techniques for compression, quantization, distillation, and privacy-preserving learning for edge and on-device AI. The director collaborates with Engineering and Product teams to translate research breakthroughs into scalable production-ready capabilities, builds, mentors, and leads a research team fostering creativity, scientific rigor, and innovation, evaluates emerging technologies, academic research, and industry trends to influence strategic direction, designs and evaluates experiments, benchmarks, and methodologies for model performance and efficiency, represents webAI in research discussions with customers, partners, and the broader AI community, and ensures research initiatives align with customer missions, security requirements, and enterprise needs.
Senior–Staff Machine Learning Researcher
Design, train, test, and iterate on diffusion models for 3D geological models. Design, train, test, and iterate on an approach for conditioning generation on geophysical data and other observations. Inform the generation of synthetic data to improve model performance. Adapt diffusion modeling approach to specific real-world projects in collaboration with project teams.
Research Intern – Reinforcement Learning (RL)
Design and build reinforcement learning environments that model real-world customer interaction workflows. Design reinforcement learning agents that learn from these environments using real-world interaction data, rewards, and feedback loops. Define reward models and feedback loops using real-world signals (outcomes and human feedback). Enable learning from production data by structuring interaction traces into training-ready datasets for offline and online learning. Experiment with multi-agent systems and simulation frameworks for complex coordination and decision-making. Collaborate with engineering and product teams to deploy, evaluate, and iterate on learning systems in production at scale.
Recruiting Programs & Operations Manager
Lead the research and development of novel deep learning algorithms that enable robots to perform complex, contact-rich manipulation tasks. Explore the intersection of computer vision and robotic control by designing systems that allow robots to perceive and interact with objects in dynamic environments. Create models integrating visual data to guide physical manipulation beyond simple grasping to sophisticated handling of diverse items. Collaborate with a multidisciplinary team to translate concepts into robust capabilities deployable on physical hardware for industrial applications. Research and develop deep learning architectures for visual perception and sensorimotor control in contact-rich scenarios. Design algorithms enabling robots to manipulate complex or deformable objects with high precision. Collaborate with software engineers to optimize and deploy research prototypes onto physical robotic hardware. Evaluate model performance in simulation and real-world environments to ensure robustness and reliability. Identify opportunities to apply state-of-the-art advancements in computer vision and robot learning to practical industrial problems. Mentor junior researchers and contribute to the technical direction of the manipulation research roadmap.
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