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. 

This 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. 

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. 

This position is based in Zurich, Switzerland (hybrid). 

Your mindset 

• Engineering-First ML Practitioner: You prioritize robustness, reliability, and maintainability over novelty. 

• Strong Software Engineer: You design modular, testable, and extensible systems and apply software engineering best practices consistently. 

• Production-Oriented Thinker: You consider latency, memory, hardware constraints, observability, and lifecycle management from day one. 

• Data-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. 

• System-Level Collaborator: You think beyond the model and understand how ML components interact with robotics, control logic, and distributed AV systems. 

• Evaluate and benchmark state-of-the-art ML models and algorithms for perception, tracking, and multimodal awareness. 

• Design and maintain reproducible evaluation pipelines measuring model performance, latency, memory footprint, and robustness. 

• Integrate ML models into production systems in collaboration with Robotics and Platform teams. 

• Optimize inference pipelines for real-time performance on constrained hardware (CPU/GPU/edge devices, Q-SYS Cores). 

• Improve model efficiency using quantization, pruning, distillation, and runtime optimization techniques. 

• Write production-grade Python (and C++ where appropriate) following clean architecture and modular design principles. 

• Contribute to CI/CD pipelines, automated testing, regression validation, and performance monitoring for ML components. 

• Ensure reproducibility, versioning, and traceability of models, datasets, and experiments. 

• Collaborate to industrialize promising prototypes into scalable production systems. 

• Work with Product and System Architects to align ML solutions with hardware and product roadmap constraints. 

• MSc or PhD in Computer Science, Engineering, Robotics, or related technical field. 

• 5+ years of hands-on experience in machine learning engineering or applied ML roles. 

• Proven experience integrating ML models into production systems. 

• Strong proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, ONNX). 

• Solid software engineering fundamentals, including modular design, code reviews, testing strategies, and CI/CD. 

• Experience optimizing models for real-time or resource-constrained environments. 

• Understanding of system-level trade-offs in latency-sensitive or distributed architectures. 

• Ability to work independently and drive technical decisions within architectural guidelines. 

• Strong communication skills and experience collaborating in cross-functional engineering teams. 

• Preferred experience with one or more of the following:  

• Experience with computer vision, tracking, or multimodal perception systems. 

• Experience with C++ in performance-critical environments. 

• Familiarity with AV systems, media pipelines, or robotics-oriented architectures. 

• Exposure to ROS, TensorRT, or MLOps tools (MLflow, Weights & Biases, Docker).