Senior Principal Applied Scientist, Industrial ML

Siemens; Bellevue (WA); On-site; USD 197,000 - 267,000; Full time

Job summary

Siemens in Bellevue, WA is seeking a Senior Principal Applied Scientist to set the scientific direction for a specialized AI pod, defining problems worth solving with ML and guiding methods, evaluation, and transition to production.

You will own hypotheses, evaluation, and the path from research to production, leading end-to-end projects across multimodal perception, computer vision, language, and time series, while mentoring engineers on rigor, reproducibility, and safety.

Qualifications

• 10+ years in applied ML or AI research with production impact.
• Strong foundation in ML theory and practice across training, evaluation, and deployment.
• Proficiency in Python and modern ML frameworks.
• Clear written and verbal communication, ability to explain complex ML concepts to engineers, product managers, and senior leaders.

Responsibilities

• Set the applied science roadmap for the pod across core AI capabilities (multimodal perception, computer vision, language, time series, control).
• Design and own evaluation and benchmarking frameworks for generative and predictive models.
• Lead applied research projects end to end, from literature review to productionization.
• Collaborate with engineers to push models into production-grade pipelines: data readiness, optimization, inference, observability.
• Influence architectural decisions with scientific evidence and tradeoff analysis.
• Mentor scientists and engineers on rigor, reproducibility, and documentation.
• Champion responsible and trustworthy AI: bias detection and model risk management.

Skills

Applied ML

Python

Production ML

Communication

Model Evaluation

Mentoring

Job description

Siemens in Bellevue, WA is seeking a Senior Principal Applied Scientist to set the scientific direction for a specialized AI pod, defining problems worth solving with ML and guiding methods, evaluation, and transition to production.

You will own hypotheses, evaluation, and the path from research to production, leading end-to-end projects across multimodal perception, computer vision, language, and time series, while mentoring engineers on rigor, reproducibility, and safety.