Stellenbeschreibungph3Overview /h3 pAs a Senior ML Engineer in the intelligent AV pod, you will be responsible for evaluating, integrating, and optimizing state‑of‑the‑art machine learning models that power the perception and awareness engine behind Q‑SYS VisionSuite. /p pThis position emphasizes strong engineering execution: systematically benchmarking external and internal models, selecting the right techniques for production constraints, and ensuring robust deployment in real‑time, resource‑constrained AV environments. /p pYou will work closely with ML, Robotics, and Software Engineers to advance VisionSuite as a reliable, maintainable, and high‑performance solution for smart meeting spaces and intelligent buildings. /p pThis position is based in Zurich, Switzerland (hybrid). /p h3Your mindset /h3 ul liEngineering‑First ML Practitioner: You prioritize robustness, reliability, and maintainability over novelty. /li liStrong Software Engineer: You design modular, testable, and extensible systems and apply software engineering best practices consistently. /li liProduction‑Oriented Thinker: You consider latency, memory, hardware constraints, observability, and lifecycle management from day one. /li liData‑Driven Evaluator Pragmatist: You treat data as a first‑class component of the system, design robust evaluation datasets, and rigorously benchmark alternatives to select solutions based on measurable trade‑offs. /li liSystem‑Level Collaborator: You think beyond the model and understand how ML components interact with robotics, control logic, and distributed AV systems. /li /ul h3Responsibilities /h3 ul liEvaluate and benchmark state‑of‑the‑art ML models and algorithms for perception, tracking, and multimodal awareness. /li liDesign and maintain reproducible evaluation pipelines measuring model performance, latency, memory footprint, and robustness. /li liIntegrate ML models into production systems in collaboration with Robotics and Platform teams. /li liOptimize inference pipelines for real‑time performance on constrained hardware (CPU/GPU/edge devices, Q‑SYS Cores). /li liImprove model efficiency using quantization, pruning, distillation, and runtime optimization techniques. /li liWrite production‑grade Python (and C++ where appropriate) following clean architecture and modular design principles. /li liContribute to CI/CD pipelines, automated testing, regression validation, and performance monitoring for ML components. /li liEnsure reproducibility, versioning, and traceability of models, datasets, and experiments. /li liCollaborate to industrialize promising prototypes into scalable production systems. /li liWork with Product and System Architects to align ML solutions with hardware and product roadmap constraints. /li /ul h3Qualifications /h3 ul liMSc or PhD in Computer Science, Engineering, Robotics, or related technical field. /li li5+ years of hands‑on experience in machine learning engineering or applied ML roles. /li liProven experience integrating ML models into production systems. /li liStrong proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, ONNX). /li liSolid software engineering fundamentals, including modular design, code reviews, testing strategies, and CI/CD. /li liExperience optimizing models for real‑time or resource‑constrained environments. /li liUnderstanding of system‑level trade‑offs in latency‑sensitive or distributed architectures. /li liAbility to work independently and drive technical decisions within architectural guidelines. /li liStrong communication skills and experience collaborating in cross‑functional engineering teams. /li liPreferred experience with one or more of the following: /li liExperience with computer vision, tracking, or multimodal perception systems. /li liExperience with C++ in performance‑critical environments. /li liFamiliarity with AV systems, media pipelines, or robotics‑oriented architectures. /li liExposure to ROS, TensorRT, or MLOps tools (MLflow, Weights Biases, Docker). /li /ul /p #J-18808-Ljbffr
