Overview:
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.
Responsibilities:
- 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.
Qualifications:
- 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).