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.
You'll Benefit FromSiemens offers a variety of health and wellness benefits to our employees. Details regarding our benefits can be found here: https://www.benefitsquickstart.com/siemens/index.html
The pay range for this position is $167,443 - $226,073 annually with a target incentive of 15% 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.
