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.
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.
Lead Electronics Engineer
You will be responsible for defining operational domains and evaluating the reliability of the AI capabilities developed in-house. You will develop and extend the state-of-the-art in uncertainty quantification and uncertainty calibration. This involves 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.
Member of technical staff - Research - Agent
Design and develop new agents and propose new research directions involving reinforcement learning and foundation models. Design, implement, and scale high-performance systems for training large-scale agents, including infrastructure, algorithms, reward models, and training environments. Collaborate with researchers and engineers to implement, test, and productionize new agent logics, learning algorithms, and system architectures. Create, manage, and scale benchmarks and evaluation systems to track agent capabilities, owning system reliability, scalability, and observability for research infrastructure. Mentor and guide engineers and researchers, establishing and enforcing engineering standards, tooling, and best practices. Conduct code and design reviews, champion technical innovation, and proactively address technical debt to accelerate R&D lifecycle.
People Business Partner - Munich
You will be responsible for defining operational domains and evaluating the reliability of the AI capabilities developed in-house. You will develop and extend the state-of-the-art 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.
Research Engineer
Design, implement, and run pre-training and post-training pipelines for action-conditioned world models and vision-language-action (VLA) models. Develop and refine training methodologies, including fine-tuning, reinforcement learning, and large-scale multimodal learning. Design and generate training and evaluation datasets from simulation, including environment setup, domain randomization, and sim-to-real transfer strategies. Build distributed training infrastructure using PyTorch, FSDP, and DeepSpeed. Work with multimodal data pipelines involving video, sensory inputs, and action sequences. Evaluate model performance using both benchmark datasets and real-world deployment metrics. Collaborate with industrial partners to adapt generative models for real-world physical AI applications. Contributions to research publications are a plus.
Access all 4,256 remote & onsite AI jobs.
Frequently Asked Questions
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.