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.
Produktionsmitarbeiter / Monteur
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 by the company, 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.
Hardware / Software CoDesign Engineer - 3P
Co-design future hardware for programmability and performance with hardware vendors. Assist hardware vendors in developing optimal kernels and support them in the compiler. Develop performance estimates for critical kernels across different hardware configurations and influence decisions on compute core and memory hierarchy features. Build system performance models at various abstraction levels and analyze them to guide decisions on scale-up, scale-out, and front-end networking. Collaborate with machine learning engineers, kernel engineers, and compiler developers to understand their requirements from high-performance accelerators. Manage communication and coordination with internal and external partners. Influence the roadmaps of hardware partners to optimize their hardware for OpenAI's workloads. Evaluate accelerators and platforms of potential partners. As the role and team scope grow, also understand and influence roadmaps for hardware partners concerning datacenter networks, racks, and buildings.
Backend Engineer- Inference Services
The Backend Engineer is responsible for leading the design and implementation of Deepgram's products, specifically developing secure, robust, and scalable services for speech processing, distributed compute orchestration, and optimized scheduling. Responsibilities include improving Deepgram's core inference services in networking, speech processing, audio transcoding, and latency and memory optimization, developing processes for measuring, building, and optimizing services to maximize system performance, debugging complex system issues involving networking, scheduling, and high performance computing, rapidly customizing backend services to support customer needs, and partnering with Product to design and implement new services, features, and products end to end.
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