• Contribute to hardening a chemistry-to-quantum resource estimation pipeline: robustness, scalability, reproducibility, and “push-button” usability. • Implement and validate stable interfaces/data contracts between core modules (inputs/outputs/metadata/schema checks). • Help automate execution of a predefined benchmark suite across both cloud GPU environments and internal HPC clusters (job submission, retries, artifact collection, deterministic configs). • Support expansion of an end-to-end scientific computation workflow by implementing integration layers, validation, and test coverage for new upstream inputs, additional computational backends, and alternative algorithmic paths within a single, consistent execution framework. • Implement a representative application workflow that exercises the pipeline across a structured set of related inputs (parameter sweeps), automates repeated executions, aggregates outputs into analysis-ready artifacts, and documents key assumptions, approximations, and major sources of error/uncertainty. • Implement secure, automated sharing of HPC outputs to cloud object storage (e.g., S3) for downstream analysis and collaboration.

• Implement governance patterns for datasets and run outputs (naming, lineage, access boundaries, catalog/volume organization). • Improve dataset upload validation and guardrails to prevent accidental modification of unrelated storage paths and to enforce consistent metadata and file structure • Build monitoring and reporting for compute usage and cost drivers (job frequency, runtime, GPU utilization proxies, storage growth, auto-termination effectiveness). • Deliver dashboards that make platform health and spend understandable to both engineers and researchers. • Refactor existing job setup / submission scripts into a maintainable, testable OOP design (clear interfaces, configuration objects, reusable clients). • Improve workflow parameter handling for the two-stage pipeline (tensor factorization stage and QRE stage) and standardize outputs for downstream analysis. • Reduce onboarding friction by abstracting authentication and setup into a single, ergonomic path (e.g., a CLI/Python entry point that validates auth, environment, and required dependencies). • Replace “follow the guide manually” with automation: preflight checks, actionable errors, and self-serve setup validation.

• Du betreust Kundenprojekte von Anfang bis Ende • Du entwickelst Softwarelösungen von Front- bis Backend • Du nutzt modernste Technologien und lernst durch verschiedene Projekte die vielen Facetten der .NET- Entwicklung kennen • Du übernimmst Verantwortung und lernst verschiedenste Branchen kennen

• Lead integration, optimization, and troubleshooting of Wireless LAN kernel drivers for NGFW platforms. • Design and enhance wireless and data plane components to meet high-performance and low latency requirements. • Collaborate with cross functional teams to drive delivery of features, improvements, and system level enhancements. • Own complex debugging, performance analysis, and root cause investigations across WLAN and networking subsystems. • Prototype and evaluate new technologies, contributing to NextGen NGFW capabilities and product innovation. • Influence architectural decisions, performance strategies, and long-term technical direction for wireless and dataplane systems.