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.
Research Scientist, Agent Robustness
As a Production AI Ops Lead, you will design and develop the production lifecycle of full-stack AI applications, support end-to-end system reliability, ensure real-time inference observability, handle sovereign data orchestration, integrate high-security software, and maintain resilient cloud infrastructure for international government partners. You will take full accountability for the long-term performance and reliability of AI use cases deployed across international government agencies, oversee the end-to-end health of the platform ensuring seamless integration between the AI core and full-stack components (APIs to UI) to maintain a responsive production-ready environment, build automated systems to monitor model performance and data drift across geographically dispersed environments for reliability, manage the technical lifecycle within diverse regulatory frameworks, lead incident response for production issues in mission-critical environments with rapid resolution and prevention guardrails, translate technical performance metrics into clear insights for senior international government officials, and partner with Engineering and ML teams to incorporate field lessons into the technical architecture and decisions of future use cases.
Safety Research Internship (Spring/Summer 2026)
As a Cohere Research Intern, you will conduct cutting-edge machine learning research, training and evaluating production large language models. You will focus on research projects aimed at making models better understood, safer, more reliable, more inclusive, and more beneficial for the world. You will disseminate your research results through the production of publications, datasets, and code. Additionally, you will contribute to research initiatives that have practical applications in Cohere's product development. The internship involves collaborating with the Modelling Safety team on implementing novel research ideas related to fairness, safety (including for multiple languages, dialects, and cultural contexts), robustness, generalisation, interpretability, safety for agents with complex read/write actions, and safety for codegen. The project details and topic will be designed collaboratively between the intern and the team, with a goal to publish a paper in a top venue and contribute to open science. The internship may be remote or onsite, with no relocation or housing provided.
AI Researcher
You will work across the model development loop, from research questions to training runs to evaluation. This includes designing and testing architecture changes and training regimes for large language models, running controlled experiments at scale and isolating causal effects, studying failure modes in reasoning, generalisation, robustness, and representation, shaping objectives, data mixtures, and optimisation choices that influence model behaviour, building and refining evaluations that measure capability and reliability, analysing training dynamics using logs, metrics, and model outputs, collaborating with ML systems engineers on distributed training and training operations, and writing clear internal notes that turn experimental results into design decisions. You will spend substantial time in code, training runs, logs, and evaluation outputs with the goal of clarity about what improves the model and why. You will work hands-on with code as a primary tool for thinking, moving between theory and implementation quickly and precisely, preferring controlled experiments over broad sweeps, using logs, metrics, and model behaviour to guide decisions, and working closely with engineering counterparts to scale and validate ideas.
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