<div class="show-more-less-html__markup show-more-less-html__markup--clamp-after-5 relative overflow-hidden"> <strong>Overview<br/><br/></strong>As 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 .<br/><br/>T his 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.<br/><br/>You 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.<br/><br/>This position is based in Zurich, Switzerland (hybrid).<br/><br/>Your mindset<br/><br/><ul><li> Engineering-First ML Practitioner: You prioritize robustness, reliability, and maintainability over novelty. </li><li> Strong Software Engineer: You design modular, testable, and extensible systems and apply software engineering best practices consistently. </li><li> Production-Oriented Thinker: You consider latency, memory, hardware constraints, observability, and lifecycle management from day one. </li><li> Data-Driven Evaluator &amp; 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><li> System-Level Collaborator: You think beyond the model and understand how ML components interact with robotics, control logic, and distributed AV systems. <br/><br/></li></ul><strong>Responsibilities<br/><br/></strong><ul><li> Evaluate and benchmark state-of-the-art ML models and algorithms for perception , tracking, and multimodal awareness. </li><li> Design and maintain reproducible evaluation pipelines measuring model performance , latency, memory footprint, and robustness. </li><li> Integrate ML models into production systems in collaboration with Robotics and Platform teams. </li><li> Optimize inference pipelines for real-time performance on constrained hardware (CPU/GPU/edge devices, Q-SYS Cores). </li><li> Improve model efficiency using quantization, pruning, distillation, and runtime optimization techniques. </li><li> Write production-grade Python (and C++ where appropriate) following clean architecture and modular design principles. </li><li> Contribute to CI/CD pipelines, automated testing, regression validation, and performance monitoring for ML components. </li><li> Ensure reproducibility, versioning, and traceability of models, datasets, and experiments. </li><li> Collaborate to industrialize promising prototypes into scalable production systems. </li><li> Work with Product and System Architects to align ML solutions with hardware and product roadmap constraints. <br/><br/></li></ul><strong>Qualifications<br/><br/></strong><ul><li> MSc or PhD in Computer Science, Engineering, Robotics, or related technical field. </li><li> 5+ years of hands-on experience in machine learning engineering or applied ML roles. </li><li> Proven experience integrating ML models into production systems. </li><li> Strong proficiency in Python and modern ML frameworks ( PyTorch , TensorFlow, ONNX). </li><li> Solid software engineering fundamentals, including modular design, code reviews, testing strategies, and CI/CD. </li><li> Experience optimizing models for real-time or resource-constrained environments. </li><li> Understanding of system-level trade-offs in latency-sensitive or distributed architectures. </li><li> Ability to work independently and drive technical decisions within architectural guidelines. </li><li> Strong communication skills and experience collaborating in cross-functional engineering teams. </li><li> Preferred experience with one or more of the following: </li><li> Experience with computer vision, tracking, or multimodal perception systems. </li><li> Experience with C++ in performance-critical environments. </li><li> Familiarity with AV systems, media pipelines, or robotics-oriented architectures. </li><li> Exposure to ROS, TensorRT , or MLOps tools ( MLflow , Weights &amp; Biases, Docker).</li></ul> </div>