Machine Learning Engineer

• Develop and maintain Python components for machine learning applications
• Integrate, fine-tune and evaluate Transformer-based models
• Help build data-processing, training and inference pipelines
• Support deployment of models into reliable applications and services
• Measure and improve model performance, latency and resource usage
• Write tests and contribute to code quality, documentation and reproducibility
• Investigate technical problems across models, software and infrastructure
• Work closely with researchers to turn experimental code into maintainable systems
• Learn how AI workloads are deployed across cloud, private and on-premises infrastructure

Junior Machine Learning Engineer

• Own the MLOps technical vision and roadmap, aligning infrastructure investments and architecture decisions with company and engineering milestones
• Lead and build an engineering team architecting, building, and optimizing high-throughput data ingestion pipelines and platform infrastructure for petabyte-scale multimodal datasets
• Mentor and grow team members through continuous feedback, career development, and technical guidance
• Design, build, and operate high-availability hybrid cloud and on-premise compute clusters
• Provide developers and researchers with a seamless, unified compute experience
• Lead continuous deployment and monitoring pipelines for ML models deployed across thousands of edge devices in the field
• Establish full model lifecycle management, including model registries, artifact versioning, automated regression testing, and real-time observability and monitoring
• Collaborate with robotics engineers to translate requirements into reliable, scalable training environments
• Evaluate and integrate MLOps tooling on cloud and on-premise compute platforms

Data & ML Ops Lead

• Build ML models that predict residual sensor error using observable signals including temperature, thermal gradients, quadrature amplitude, drive signals, and sensor diagnostics
• Define rigorous validation protocols across unseen thermal profiles and physical sensor units to demonstrate genuine generalisation
• Benchmark learned approaches against a tuned classical baseline combining per-unit thermal compensation and adaptive Kalman filtering
• Quantify the observability boundary and identify which errors are predictable from available measurements and which are fundamentally outside the model's reach
• Train and evaluate models offline using sensor characterisation data, separating meaningful physical correlations from artefacts and overfitting
• Work with FPGA and DSP engineers to translate successful approaches into lightweight, frozen models suitable for low-latency embedded deployment
• Communicate results clearly, including when the evidence shows that a classical approach remains the better solution

Machine Learning Engineer jobs in Switzerland

Junior Machine Learning Engineer

Data & ML Ops Lead

Applied Machine Learning Engineer