AI Product Manager
As an AI Product Manager, you will define and drive the AI product roadmap ensuring alignment with business objectives and user needs. You will collaborate with cross-functional teams including engineering, design, and marketing to develop and launch AI-powered features. Responsibilities include conducting market research and analyzing user feedback to identify opportunities for AI integration, working closely with data scientists and machine learning engineers to optimize AI models for accuracy, performance, and user impact, defining key performance indicators to measure success and iterating based on data-driven insights, staying up to date with AI trends, emerging technologies, and best practices, and ensuring ethical AI usage and compliance with data privacy regulations. This role is fully onsite in Lisbon, involving close in-person collaboration in a dynamic and fast-paced environment.
Staff Software Engineer
As a Staff Software Engineer on the Perception team, you will be responsible for defining and driving the long-term vision and architecture for perception systems, architecting complex, scalable, and robust end-to-end perception and robotics systems for deployment on real-world hardware, ensuring their successful integration into Hayden’s core product platform. You will spearhead the architectural design, implementation, and long-term ownership of next-generation perception systems, transition research prototypes to production solutions, deliver high-performance, tested, and maintainable C++ code optimized for edge and robotics platforms, architect and optimize real-time perception pipelines, drive the integration of state-of-the-art ML and CV models, provide technical leadership in complex problem domains, collaborate with Product leadership and Engineering organizations, and contribute to foundational shared infrastructure, tooling, and architectural patterns to scale pilot initiatives into core product capabilities.
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 Product Manager, Rome
Define and drive the AI product roadmap, ensuring alignment with business objectives and user needs. Collaborate with cross-functional teams, including engineering, design, and marketing, to develop and launch AI-powered features. Conduct market research and analyze user feedback to identify opportunities for AI integration. Work closely with data scientists and machine learning engineers to optimize AI models for accuracy, performance, and user impact. Define key performance indicators (KPIs) to measure success and iterate based on data-driven insights. Stay up to date with AI trends, emerging technologies, and best practices to ensure our products remain competitive. Ensure ethical AI usage and compliance with data privacy regulations.
Machine Learning PhDs - AI Trainer
Use machine learning expertise to create domain-relevant questions and review AI-generated responses for accuracy, rigor, and relevance to real-world physics research and practice.
Researcher, Safety & Privacy
The role involves designing and implementing privacy-first architectures to detect and mitigate harmful model behaviors, building frameworks for auditable private identification of high-risk content such as jailbreaks, cyber threats, or weaponization instructions, and developing strict, auditable mechanisms that are triggered only by harm signals. Additionally, the researcher will drive the development of automated safety systems that preserve privacy at every level, operationalizing frameworks for identifying and addressing frontier risks while ensuring privacy guarantees remain intact even under adversarial conditions, and working on foundational problems including privacy-preserving monitoring, algorithmic auditing, secure enclaves, and adversarially robust safety enforcement protocols.
Senior Computer Vision Engineer (Autonomous Driving)
As a Senior Computer Vision Engineer at 42dot, responsibilities include researching and developing 3D computer vision and machine learning algorithms for autonomous driving technology, performing 3D shape modeling and processing, implementing object pose estimation and tracking algorithms, developing efficient and scalable vision solutions, exploring the intersection of vision and robotics, working on low-level and physics-based vision algorithms, conducting self-supervised representation learning from large-scale unlabeled scene data, and creating world models and closed-loop simulation for autonomous driving.
Director of Biomarkers and Experimental Medicine
Develop and advance machine learning models for biological, preclinical, and translational datasets, including multimodal omics, imaging, text, and assay data; design and implement scalable pipelines for data curation, training, evaluation, and inference integrated into discovery workflows; own projects end-to-end from problem framing to prototyping, validation, and deployment; evaluate robustness, reliability, and interpretability of models to support scientific decision-making; contribute technical leadership by proposing new directions, shaping platform capabilities, and raising engineering and research standards through collaboration.
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.
Research Intern – Reinforcement Learning (RL) - Onsite
Design and build reinforcement learning environments that model real-world customer interaction workflows. Design RL 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 such as 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.
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