Siemens builds the systems the physical world runs on: factories, power grids, buildings, trains, hospitals. Industrial and physical AI is a major opportunity in applied AI, and one of the harder ones to get right. There is a generation of AI-powered products to build.

We are an applied science organization building the science behind them. The work sits at the intersection of machine learning research, real world data, and production systems running in industrial environments.

As Applied Scientist, you work on the science on a major capability area inside the pod. You take ambiguous problems through hypothesis, method selection, experimentation, and into models that run in production. You are hands on with the modeling, and you are accountable for the rigor and the outcome.

This is an individual contributor role. You will work in close partnership with engineers and product managers who turn your work into product.

Key responsibilities

• Contribute to one or more scientific capability areas end to end, for example perception, computer vision, language and agents, time series, control, planning, or evaluation

• Take problems from ambiguous product or system requirements through clear research questions, hypotheses, and success metrics

• Contribute to applied research projects: literature review, method selection, experimentation, ablation, error analysis, and productization

• Build and run the evaluation pipelines for the work you own: offline metrics, online experiments, robustness testing in industrial conditions

• Work with engineers to take models into production grade pipelines: data readiness, training infrastructure, inference, observability

• Make scientific tradeoffs in front of engineers and product managers, with evidence, and translate them into decisions the team can act on

• Identify and de-risk scaling challenges in your area: data quality, model drift, latency, throughput, cost, safety

• Raise the bar on experimentation rigor, reproducibility, and documentation across the team

• Apply responsible AI practices in your work: bias detection, model risk management, human in the loop controls

Basic qualifications

• 4+ years in applied machine learning, AI research, or data science, with models that shipped to production and made an impact

• Strong foundation in machine learning theory and practice across training, evaluation, and deployment

• Demonstrated experience taking research from a paper or prototype into a model that runs reliably in production

• Proficiency in Python and modern ML frameworks and toolchains

• Strong partnership track record with engineering teams on data, training infrastructure, and inference

• Clear written and verbal communication with engineers, product managers, and senior leaders

Preferred qualifications

• Experience applying ML in industrial or physical domains: manufacturing, automation, robotics, energy, mobility, infrastructure, healthcare

• Deep expertise in one of: multimodal ML, generative AI, retrieval augmented generation, agentic workflows, time series, control, or planning

• Scientific ML for physical systems: surrogate modeling, operator learning, physics-informed ML, geometry-aware ML, differentiable simulation, AI for semiconductor/EDA

• Hands-on experience building evaluation pipelines, running online experiments, or instrumenting production monitoring for a model you owned

• Publications, patents, open source contributions, or significant internal technology transfers

• Experience working with globally distributed research, product, or engineering organizations