Senior Principal Applied Scientist; f​/m​/d

Senior Principal Applied Scientist (f/m/d)

Location(s):

• Berlin
  - Berlin
  - Germany

As Senior Principal Applied Scientist, you set the scientific direction for the pod. You decide which problems are worth solving with ML, what methods to apply, how to evaluate them, and how to move them from research results to models that run in production. You are accountable for the science: the rigor, the evidence, and the outcomes. This is a senior individual‑contributor role;

your impact comes from owning the hypotheses, evaluations, and the path from research to production, and from raising the scientific bar across the team.

Responsibilities

• Set the applied science roadmap for the pod across core AI capabilities (e.g., multimodal perception, computer vision, language, agentic reasoning, time‑series modeling, control).
• Convert product and system requirements into clear research questions, hypotheses, and success metrics.
• Design and own the evaluation and benchmarking frameworks for generative and predictive models, including offline metrics, online experimentation, and robustness testing in industrial conditions.
• Lead applied research projects end to end, from literature review and method selection through experimentation, ablation, and productization.
• Work with engineers to take models into production‑grade pipelines: data readiness, optimization, inference, observability.
• Influence architectural and system decisions with scientific evidence and trade‑off analysis.
• Identify and de‑risk scaling challenges: data quality, model drift, latency, throughput, cost, safety.
• Mentor scientists and engineers on experimentation rigor, reproducibility, and documentation.
• Champion responsible and trustworthy AI: bias detection, model risk management, human‑in‑the‑loop controls.

Qualifications

• Master’s degree or equivalent in Computer Science, Machine Learning, or a related field.
• 10+ years in applied machine learning, AI research, or data science, with a track record of models shipped to production and making impact.
• Strong foundation in machine learning theory and practice across training, evaluation, and deployment.
• Demonstrated experience setting the science direction for a portfolio of work and shipping it through to production with engineering teams.
• Proficiency in Python and modern ML frameworks and tool chains.
• Track record of building evaluation and benchmarking that the team can run a roadmap against.
• Clear written and verbal communication, with the ability to explain complex ML concepts to engineers, product managers, and senior leaders.
• Preferred:
  Experience setting science direction across multiple capability areas simultaneously; experience bringing applied research into real‑world industrial or physical products; breadth across multimodal ML, generative AI, retrieval, agentic workflows, control, or planning; experience standing up evaluation, online experimentation, or production monitoring as practices; publications, patents, or significant internal technology transfers; hiring and mentoring senior or staff‑level scientists; experience working with globally distributed research, product, or engineering teams.
• Business proficiency in English.

Benefits

• An attractive remuneration package.
• Access to employee share plans and appealing Siemens pension benefits.
• 30 days of paid vacation and 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 to 30 days workation per year in certain countries.

We are an equal‑opportunity employer. We are happy to consider applications from individuals with disabilities.

About us

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.