• Architektur und Entwicklung verteilter Backend-Systeme zur Verarbeitung und Archivierung von Sensordaten (bspw. Python, MongoDB, MQTT, RabbitMQ) • Design und Implementierung skalierbarer Datenpipelines und Datenplattformen • Entwicklung moderner Angular-basierter Webanwendungen zur Visualisierung großer Datenmengen sowie CI/CD-Pipelines und Container-Infrastruktur (GitLab, Docker) • Entwicklung und Integration von Embedded- und Edge-Systemen (z. B. NVIDIA Jetson, Raspberry Pi) • Aufbau und Weiterentwicklung der bestehenden Softwarearchitektur und Entwicklungsprozesse • Planung und Umsetzung von automatisierten Tests und Qualitätssicherungsprozessen.

• Architecture, development and operation of distributed backend systems for processing and archiving sensor data (Python, MongoDB, MQTT, RabbitMQ) • Design and operation of scalable Linux-based server and system architectures • Development and securing of distributed communication and software systems with respect to robustness, fault tolerance, and cybersecurity • Development of modern web applications for visualization and analysis of large datasets (Angular, TypeScript) • Building and operating containerized development and deployment environments (Docker, GitLab CI/CD) • Development and integration of embedded and edge systems (e.g., NVIDIA Jetson, Raspberry Pi) • Planning and execution of systematic testing, validation, and quality assurance processes for applications in critical infrastructure environments • Further development of existing software architectures and development processes

• Define and build evaluation methodologies for Deepgram's models, spanning speech-to-text, text-to-speech, and emerging LLM, RAG, agent, and multimodal systems. • Design, build, and maintain automated evaluation pipelines across batch and streaming (e.g. WER, runaway/hallucination detection, latency and time-to-first-byte), with a focus on correctness, reproducibility, and ease of adoption. • Build scalable, reproducible evaluation infrastructure — harnesses, orchestration, and result-aggregation pipelines — running against production models and, where needed, large GPU clusters. • Translate Research benchmarks and expected model metrics into automated, enforceable pass/fail gates. • Build and operate canaries and continuous-monitoring systems that detect quality regressions in production before they reach customers. • Partner with DevOps/Infra to stand up ephemeral test environments and results-aggregation infrastructure. • Work alongside Research, model training, inference, and product teams to provide trusted evaluation signals that inform release and optimization decisions. • Integrate evaluation and quality gates into CI/CD so quality is verified continuously, not manually. • Help raise the bar through code reviews, technical design discussions, and strong engineering and QA practices.

• Provide expert-level support for ECAD tools focusing on schematic capture and PCB layout workflows • Develop and maintain scripts to automate design tasks, improve data integrity, and streamline the design-to-manufacturing flow • Manage and maintain Linux-based EDA environments, provide secondary support for Windows-based design workstations • Manage EDA license servers, monitor usage, and troubleshoot checkout issues • Coordinate tool upgrades, patch rollouts, validation, and user communication and training • Create and maintain technical documentation, SOPs, internal best practices, support knowledge bases, and user guides