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Transform the everyday Siemens builds the systems the physical world runs on: factories, power grids, buildings, trai...

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Transform the everyday Siemens builds the systems the physical world runs on: factories, power grids, buildings, trai...

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Transform the everyday Siemens builds the systems the physical world runs on: factories, power grids, buildings, trai...

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Transform the everyday Siemens builds the systems the physical world runs on: factories, power grids, buildings, trai...

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Selected role

Applied Scientist

Siemens München, BY, DE

$300k Senior Full-time

Transform the everyday

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.

As part of our team, you will have

An attractive remuneration package
Appealing Siemens pension benefits
Access to employee share plans
30 days of paid vacation and a variety of flexible work schedules that allow time off for you and your family
2 to 3 days of mobile working per week as a global standard
Up tp 30 days workation per year in certain countries

Since each of over 300,000 team members feels that other benefits are particularly important, and we cannot list our entire benefit portfolio here, you can find more information here.

The individual benefits are subject to regulatory, contractual, or corporate conditions.

Y*ou’ll make an impact by*

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

This is how you'll win us over

Education
Master´s degree or an equivalent qualification in Computer Science, Machine Learning, or a related field
Experience & Skills
At least 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
Ways of working: You work as part of an applied science team at the intersection of research, real-world data, and production systems, with strong collaboration across function
Languages: business proficiency in English

You are much more than your qualifications, and we believe in the potential of every single candidate. We look forward to getting to know you!

Your individual personality and perspective are important to us. We create a working environment that reflects the diversity of the society and support you in your personal and professional development. Let’s get to know your authentic personality and create a better future together with us. As an equal-opportunity employer we are happy to consider applications from individuals with disabilities.

About us

To transform the everyday, you need to think outside the box. That’s why we at Siemens are looking for innovators who aren’t afraid to push boundaries to join our diverse team of tech experts. Got what it takes? Then help us create lasting, positive change!

www.siemens.de/careers – if you would like to find out more about jobs & careers at Siemens.

FAQ – if you need further information on the application process.

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