Senior Applied Scientist at Siemens

Job description

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 Senior Applied Scientist, you lead 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 a senior 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 • Lead 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 • 6+ 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 mentoring more junior scientists and engineers • Experience working with globally distributed research, product, or engineering organizations About the Team: We are an early-stage engineering team solving hard technical problems at the intersection of AI, systems, and real-world interaction. We operate with high autonomy, collaborate closely across functions, and maintain high technical standards. Principal Engineers at our company are expected to lead by example, model engineering excellence, and shape both what we build and how we build it. Our Commitment to Equity and Inclusion in our Diverse Global Workforce: We value your unique identity and perspective. We are fully committed to providing equitable opportunities and building a workplace that reflects the diversity of society, while ensuring that we attract the best talent based on qualifications, skills, and experiences. We welcome you to bring your authentic self and transform the everyday with us. You’ll Benefit From Siemens offers a variety of health and wellness benefits to our employees. Details regarding our benefits can be found here: benefitsquickstart.com The pay range for this position is $167,443 - $226,073 annually with a target incentive of 10% of the base salary. The actual wage offered may be lower or higher depending on budget and candidate experience, knowledge, skills, qualifications, and premium geographic location. Equal Employment Opportunity Statement Siemens is an Equal Opportunity Employer encouraging inclusion in the workplace. All qualified applicants will receive consideration for employment without regard to their race, color, creed, religion, national origin, citizenship status, ancestry, sex, age, physical or mental disability unrelated to ability, marital status, family responsibilities, pregnancy, genetic information, sexual orientation, gender expression, gender identity, transgender, sex stereotyping, order of protection status, protected veteran or military status, or an unfavorable discharge from military service, and other categories protected by federal, state or local law. EEO is the Law Applicants and employees are protected from discrimination on the basis of race, color, religion, sex, national origin, or any characteristic protected by Federal or other applicable law. Reasonable Accommodations If you require a reasonable accommodation in completing a job application, interviewing, completing any pre-employment testing, or otherwise participating in the employee selection process, please fill out the accommodations form by clicking on this link Accommodation for disability form . If you’re unable to complete the form, you can reach out to our AskHR team for support at 1-866-743-6367. Please note our AskHR representatives do not have visibility of application or interview status. Pay Transparency Siemens follows Pay Transparency laws. Application Deadline This role will be posted for a minimum of five (5) days from the original posting date. The posting timeline may change based on applicant volume to ensure we attract and consider a strong and representative candidate pool. California Privacy Notice California residents have the right to receive additional notices about their personal information. To learn more, click here . Criminal History Qualified applications with arrest or conviction records will be considered for employment in accordance with applicable local and state laws.

Requirements

Must-haves

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.
This is a senior individual contributor role. You will work in close partnership with engineers and product managers who turn your work into product.
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
Lead 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
6+ 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

Nice-to-haves

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 mentoring more junior scientists and engineers
Experience working with globally distributed research, product, or engineering organizations

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningpythonml

Platform: avature
Effort: medium

Benefits

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Vision

Wellness