Senior Platform/DevOps Engineer (Kubernetes-Linux)
Translate business requirements into requirements for AI/ML models; prepare data to train and evaluate AI/ML/DL models; build AI/ML/DL models by applying state-of-the-art algorithms, especially transformers; leverage existing algorithms from academic or industrial research when applicable; test, evaluate, and benchmark AI/ML/DL models and publish the models, data sets, and evaluations; deploy models in production by containerizing them; work with customers and internal employees to refine model quality; establish continuous learning pipelines for models using online or transfer learning; build and deploy containerized applications on cloud or on-premise environments.
AI Factory Customer Engineer
The AI Factory Customer Engineer is responsible for translating business requirements into AI/ML model requirements, preparing data to train and evaluate AI/ML/DL models, building AI/ML/DL models using state-of-the-art algorithms, particularly transformers, and leveraging existing algorithms from research. They test and evaluate the models, benchmark their quality, and publish models, datasets, and evaluations. This role includes deploying models in production through containerization, working with customers and internal teams to refine model quality, establishing continuous learning pipelines for models with online or transfer learning, and building and deploying containerized applications on cloud or on-premise environments.
Senior Mission Success Engineer, US Federal
The responsibilities for the Senior Mission Success Engineer include translating business requirements into AI/ML model requirements, preparing data for training and evaluating AI/ML/DL models, building AI/ML/DL models using state-of-the-art algorithms, testing and benchmarking the quality of these models, publishing models, data sets, and evaluations, deploying the models in production by containerizing them, working with customers and internal teams to refine model quality, establishing continuous learning pipelines with online or transfer learning, and building and deploying containerized applications on cloud or on-premise environments.
Senior Forward Deploy Engineer
The role involves translating business requirements into AI/ML model requirements, preparing data to train and evaluate AI/ML/DL models, building AI/ML/DL models by applying state-of-the-art algorithms including transformers, testing and evaluating these models, benchmarking their quality, and publishing the models, datasets, and evaluations. Responsibilities also include deploying models in production by containerizing them, working with customers and internal employees to refine model quality, establishing continuous learning pipelines using online or transfer learning, and building and deploying containerized applications on cloud or on-premise environments.
Hydronic Systems Architect (Mechanical Distribution Focus)
Translating business requirements into requirements for AI/ML models. Preparing data to train and evaluate AI/ML/DL models. Building AI/ML/DL models by applying state-of-the-art algorithms, especially transformers, and leveraging existing algorithms from academic or industrial research as needed. Testing and evaluating AI/ML/DL models, benchmarking their quality, and publishing the models, datasets, and evaluations. Deploying models in production by containerizing them. Working with customers and internal employees to refine model quality. Establishing continuous learning pipelines for models with online learning or transfer learning. Building and deploying containerized applications on cloud or on-premise environments.
Customer Engineer, Leviathan
Responsibilities include translating business requirements into AI/ML model requirements, preparing data for training and evaluation of AI/ML/DL models, building AI/ML/DL models using state-of-the-art algorithms (especially transformers), leveraging existing algorithms from academic or industrial research when applicable, testing and evaluating models, benchmarking quality, publishing models and data sets, deploying models in production by containerizing them, working with customers and internal employees to refine model quality, establishing continuous learning pipelines for models with online or transfer learning, and building and deploying containerized applications on cloud or on-premise environments.
Customer Engineer, Leviathan
The role involves translating business requirements into AI/ML model requirements, preparing data for training and evaluating AI/ML/DL models, building models using state-of-the-art algorithms including transformers, testing and evaluating models, benchmarking their quality, and publishing models, data sets, and evaluations. Responsibilities also include deploying models in production by containerizing them, working with customers and internal employees to refine model quality, establishing continuous learning pipelines using online or transfer learning, and building and deploying containerized applications on cloud or on-premise environments.
Engineering Manager, Distillation & Dectection Platform
Lead a team of software engineers building detection and mitigation systems for frontier model misuse, focusing on model IP protection, distillation detection, and emerging risks from autonomous agents. Set the technical roadmap and execution strategy including prioritization, design, shipping, iteration, and impact measurement. Build production systems such as services, pipelines, tooling, instrumentation, and automation that can scale with frontier model usage. Partner with Research and Product teams to translate evolving model capabilities into scalable tests, signals, and mitigations. Drive strong engineering fundamentals including architecture, reliability, monitoring, performance, and operational excellence. Hire and grow a team across backend, data systems, and applied ML engineering domains. Anticipate and address scalability challenges as agentic workflows advance.
Senior Program Manager, Beta Test
Contribute to design, build, and maintain services that power AI-driven applications, ensuring scalability and performance. Develop APIs and microservices that facilitate seamless integration between cloud-based AI models and edge devices. Optimize data pipelines and storage solutions for real-time AI inference and processing. Work closely with AI researchers, infrastructure engineers, and frontend developers to deliver end-to-end AI-driven solutions. Build and optimize an agent orchestration runtime that enables tool use, memory management, and multi-step reasoning across LLMs, APIs, and edge-connected systems. Support the implementation of security and privacy best practices for distributed AI systems.
Schichtleiter in der Produktion
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.
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