锦鲤求职岗位描述
The role
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 forming engineering pods in China to build them. As Principal Engineer, you own the technical vision and system architecture for the AI-powered platforms the pod ships. You take evolving product and research requirements and turn them into systems that run reliably, scale economically, and stay maintainable as the work grows.
This is a senior individual contributor role for a deeply technical engineer who thrives in ambiguity. You are hands on. Your primary impact is through architectural leadership, technical judgment, and raising the engineering bar across the team.
Key responsibilities
Define the technical vision and system architecture for AI-powered platforms and products in the pod
Convert evolving product and research requirements into scalable, reliable ML systems
Partner with the Senior Principal Product Manager to align technical decisions with product strategy
Partner with the Senior Principal Applied Scientist and Principal Scientists to take models from experimentation into production grade systems
Own architectural decisions across model training, inference, data pipelines, and system integration
Identify critical risks early in performance, scalability, cost, and reliability, and drive solutions
Lead technical design reviews and influence architecture across multiple engineering teams
Establish best practices for ML system design, observability, testing, and long-term maintainability
Mentor senior engineers and serve as a technical role model in the organization
Basic qualifications
8+ years of professional software engineering experience, including significant work on AI or ML-powered systems
Demonstrated experience designing, building, and scaling complex distributed systems
Strong understanding of the end-to-end machine learning lifecycle, including deployment and monitoring in production
Demonstrated ability to lead architectural efforts and influence technical direction beyond your immediate team
Proficiency in at least one backend systems programming language: Python, Go, Java, or similar
Strong system-level reasoning across performance, scalability, fault tolerance, and cost tradeoffs
Preferred qualifications
Experience bridging machine learning research and production engineering
Familiarity with generative AI systems, large language models, or multimodal pipelines
Experience building systems that interact with the physical world or real time environments
Background in ML infrastructure, model serving, inference optimization, or training infrastructure at scale
Experience mentoring senior engineers or acting as a technical lead across teams
Prior collaboration with globally distributed engineering or research organizations