Python programozó diákmunka állások, munkák

• Design, build, and operate system-level infrastructure for autonomous test vehicles
• Take end-to-end responsibility for components, OS provisioning, deployment and runtime behavior
• Debug complex issues across Linux systems, networking and hardware components

• Develop and optimize optical system designs for automotive applications, with a focus on holographic display architectures
• Translate system requirements into robust optical concepts and evaluate their performance, including tolerancing and straylight analysis

• Partner with internal teams and model researchers to evaluate high-value use cases and understand their business context.
• Analyze use cases across the organization and advise on how to make them work effectively with our tabular foundation models.

• Investigate and resolve data inconsistencies across complex end-to-end data pipelines
• Automate complex analytics across large datasets to validate outputs and identify anomalies or inconsistencies
• Debug pipeline failures and improve overall pipeline robustness

• Own and manage the fab CIM ecosystem, including MES transformation and factory automation systems
• Oversee equipment integration and communications across manufacturing systems
• Define and execute the strategic roadmap for manufacturing systems within a high-volume wafer fab environment

• Ensure state of the art testing across embedded, backend, and frontend domains to ensure high quality software products
• Create the test approach based on business and technical objectives
• Develop the test automation framework in close collaboration with other experts

You will be part of the quality assurance team of our platform — designing and running tests, qualifying hardware, and making sure everything we ship performs flawlessly on Linux and macOS. When customers run into trouble, you're also the person who helps them get back on track, and makes sure what...

• Translate R&D business and user requirements into effective system, process, and automation solutions
• Design, implement, test, and continuously improve automated workflows, AI-augmented data flows and AI Agents
• Ensure stable data flows, high data quality, and reliable system operations

Drive SIEM maturity alongside our Tech Lead by continuously developing our Google Chronicle & SOAR platform, building and tuning correlation rules, and designing automated playbooks to optimize detection engineering....

• Analyze existing manufacturing and product-level data to identify structures and relationships.
• Design a structured data model to organize product-relevant data.
• Explore and link data to support product-level carbon footprint analysis.
• Develop queries and small-scale prototypes to test the data model.

• Provide technical support to scientific and industrial customers via phone, webchat, and email, focusing on post-sales and service inquiries
• Collaborate with local Engineering and global Technical Support teams to deliver accurate solutions and guidance

• Build agent loops and orchestration: tool use, structured outputs, business-rule enforcement, graceful failure
• Build the integration layer (via MCP) that lets agents securely call backend systems and business APIs
• Render model output into live, streamed, accessible UI via generative-UI protocols

• Build agent loops and orchestration: tool use, structured outputs, business-rule enforcement, graceful failure
• Build the integration layer (via MCP) that lets agents securely call backend systems and business APIs
• Render model output into live, streamed, accessible UI via generative-UI protocols

Design, build, and maintain scalable data platforms and data & AI solutions tailored to business needs, including the development of governance structures and access control concepts...

• Evaluation of different technologies in the field of cellular, NB-IoT and sub giga based communication standards
• Support the selecting process for MEMS and IMU's with focus on ultra-low power use cases

• Drive pioneering technical advances to the field of foundation models on structured data, as well as exploring world models for solving business problems.
• Expand the horizon of what is possible by bringing together rich semantic context with large scale business data

• Define and evolve data models for R&D entities
• Design and implement robust data pipelines and automated data quality checks
• Improve traceability and metadata management so data can be understood, trusted, and reused across teams

• Definition and improvement of metrics reflecting product performance, service quality, and operational efficiency
• Analysis of client behavior and service processes to identify trends, bottlenecks, inefficiencies, and drivers of performance

• Designing and implementing end-to-end data flows from device data ingestion through transformation, storage and consumption
• Defining data models, structures and lifecycle concepts to ensure data quality, consistency and lineage

• Development of APIs for electrolysis systems and analysis devices
• Implementation of autonomous process control
• Conceptualization and implementation of degradation models for electrolysis
• Studying performance and degradation of electrolysis in dependence on different operating modes