20 beste Stellen als sr. machine learning engineer in zürich zh – aktuell auf Personalsuche

Sr. Machine Learning Engineer
QSC, LLC —Zürich, ZH
- 100%
- Doktorat
- Master
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Sr. GenAI & ML Specialist Solutions Architect, ASPI Auto & Manufacturing
AWS EMEA SARL (Switzerland Branch) - G96 —Zürich, ZH
- 100%
- Doktorat
- Englisch
- Master

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Sr. Machine Learning Engineer

QSC, LLC -
Zürich, ZH

Jetzt bewerben

Details zur Stelle

100% | Pensum: 90-100%

Qualifikationen

CI/CD
Doktorat
Master
C++
Docker
Kommunikationsfähigkeit
Python

Vollständige Stellenbeschreibung

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).

Jetzt bewerben

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