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.
PhD Research Intern, Multi-Modal Foundation Encoder for Perception
During this internship, the intern will lead the development of a multi-modality (vision, LiDAR, Radar, and language), temporal foundation encoder to support 3D object detection & tracking, 3D segmentation (occupancy), and live maps. The research will aim to significantly improve system performance on long-tail events and rare classes by utilizing a large-capacity foundation model to learn rich representations across different sensor modalities. The project also aims to improve perception in adverse environmental conditions such as medium to heavy rain and fog, reduce false positives on water splashes or dust particles, achieve long-range sensing for highway driving, and build robustness to occlusion. The role includes exploring novel directions such as tri-modal foundation models with self-supervised pre-training, radar-language grounding for zero-shot detection, efficient sensor fusion via sparse cross-attention, or integrating 3D Gaussian Splats for dynamic agent geometry and streaming sparse Gaussian occupancy prediction.
People Partner
The role involves defining operational domains and evaluating the reliability of AI capabilities developed in-house. Responsibilities include developing and extending methods for uncertainty quantification and uncertainty calibration, understanding the AI systems built by the company, interfacing with these systems, and evaluating their robustness in real-world and adversarial scenarios. The position requires contributing to impactful projects and collaborating with people across multiple teams and backgrounds.
Research Scientist
The Research Scientist will investigate how intervening on training data can improve the quality and behavior of deep learning models. Responsibilities include sourcing, vetting, implementing, and improving ideas from the research literature and personal insights, conducting research guided by real customer needs rather than conference benchmarks, and collaborating closely with engineers and product teams to turn research findings into tangible impact. The role requires working autonomously in a fast-moving startup environment, engaging with customers, and contributing to shaping the product vision.
Compensation and Analytics Program 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, designing systems that allow robots to perceive and interact with objects in dynamic environments. Create models that integrate visual data to guide physical manipulation, moving beyond simple grasping to sophisticated handling of diverse items. Collaborate with a multidisciplinary team of engineers and researchers to translate cutting-edge 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 for high precision manipulation of complex or deformable objects. 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 computer vision and robot learning advancements to practical industrial problems. Mentor junior researchers and contribute to the technical direction of the manipulation research roadmap.
PhD Research Intern, Offline Driving Intelligence
Interns on the Offline Driving Intelligence team will develop state-of-the-art agent policies, contribute to publishable research, and receive mentorship from experienced researchers. They will work with a mentor to address a major open research question currently facing the team. Their research may directly be used in production as part of the simulation system that tests Zoox's autonomous driving software.
Material Data Specialist
You will be responsible for defining operational domains and evaluating the reliability of the AI capabilities developed in-house. You will develop and extend state-of-the-art methods in uncertainty quantification and uncertainty calibration. This will involve understanding the AI systems built, interfacing with them, and evaluating their robustness in real-world and adversarial scenarios. You will contribute to impactful projects and collaborate with people across several teams and backgrounds.
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.
Abuse Investigator (AI Self-Improvement Risk)
As an Abuse Investigator focused on AI Self-Autonomy and Agentic Risk on the Intelligence and Investigations team, you will be responsible for identifying and investigating cases where models exhibit autonomous or agentic behavior, including chaining capabilities, acting with increasing independence, or demonstrating patterns that may introduce safety risk. This includes detecting behaviors that are not explicitly intended, understood, or covered by existing safeguards. You will review leads, investigate model behavior, and identify cases where systems demonstrate agentic or autonomous patterns that introduce safety risks. You will detect and analyze behaviors such as multi-step planning, capability chaining, tool use, persistence, and workaround behavior. You will develop signals and tracking strategies to help proactively identify emerging agentic risk patterns across the platform. You will identify gaps in existing safeguards, evaluations, or monitoring systems and propose improvements. You will communicate investigation findings clearly to technical, policy, and leadership stakeholders. This role involves working in high-pressure environments and interacting with others effectively.
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