• Lead the development of tools and processes using LLMs, Diffusion Models, and AI Agents • Oversee the deployment of AI-driven models for enhancing content creation • Evaluate the impact of solutions for engineering and AI excellence • Collaborate with cross-functional teams for aligning technical initiatives • Hire and inspire a high-performing team of AI professionals • Foster a culture of continuous learning and innovation • Provide strategic guidance and facilitate informed decision-making • Communicate team progress and achievements to senior leadership

• Own and evolve our multi-engine inference platform, supporting a variety of model types and serving requirements. • Build and improve production ML pipelines — taking models from experimentation to reliable, high-throughput serving. • Define and implement model versioning, rollout, rollback, and lifecycle management strategies that ensure reproducibility and operational reliability. • Define and enforce serving-layer SLAs, including latency, availability, GPU utilization, Time-to-First-Token (TTFT), and Inter-Token Latency (ITL). • Build observability, monitoring, alerting, and operational tooling for production inference systems. • Apply software engineering best practices, including testing, CI/CD integration, and reproducibility across ML workflows. • Optimize inference performance through efficient resource utilization, hardware-aware serving strategies, and cost-conscious infrastructure design. • Ensure ML serving systems are secure, scalable, and operationally resilient. • Partner with ML, Data, Product, and DevOps teams to turn ideas into production systems, driving the technical decisions on serving and scale.

• Design, build, and operate Samsara’s end-to-end ML platform spanning training, experimentation, batch and online inference, and edge deployment • Partner with product and applied ML teams to design, launch, and iterate ML-powered features (e.g., backend CV models, EcoDriving insights, LLM-based reporting) • Lead throughput and cost estimation for new ML features—from early-stage exploration to production-scale capacity planning • Collaborate on experiment design and evaluation, including defining success metrics, structuring A/B tests or offline evaluations • Evolve shared training and experimentation infrastructure (e.g., job orchestration, cluster configuration, environment management) • Design and operate scalable online and batch inference systems (Ray- and Spark-based) • Partner with firmware and edge teams to define workflows for packaging, validating, and deploying models to Samsara devices • Own the reliability, observability, and security posture of ML systems across cloud and edge environments • Provide Staff+/Senior-Staff-level technical leadership by setting architecture and strategy for ML infrastructure • Drive strong developer experience through documentation, office hours, and best practices • Own or co-own end-to-end technical delivery for high-priority or high-risk initiatives.

• Weiterentwicklung leistungsstarker Computer-Vision-Architekturen zur Erkennung und Diagnose medizinischer Befunde in komplexen radiologischen Datensätzen (MRT, CT). • Konzeption und Implementierung rigoroser Validierungsframeworks zur Sicherstellung von Modellrobustheit, klinischer Wirksamkeit und Konformität mit Standards der Medizinprodukte-Zertifizierung. • Entwicklung von Lösungen mit klinischem Vertrauen im Fokus, insbesondere durch Modellinterpretierbarkeit und Unsicherheitsquantifizierung, um umsetzbare Erkenntnisse für medizinisches Fachpersonal zu liefern. • Optimierung skalierbarer ML-Pipelines in einer modernen Docker- und AWS-Umgebung, um einen reibungslosen Übergang von experimenteller Forschung zu produktionsreifen Deployments zu gewährleisten.