Principal Software Engineer

Siemens•New York, NY
•$200,614 - $271,483•Remote

About The Position

We are developing AI-native products that bring generative intelligence into the physical world. As a Principal Engineer, you will set the technical direction for systems that blend cloud infrastructure, machine learning, and hardware interaction. You will be responsible for defining architecture that is robust, scalable, and adaptable to rapid research and product iteration. This is a high-impact role for a technical leader who thrives on deep systems work and cross-disciplinary problem solving. You will work closely with engineers, ML researchers, product managers, and executives to define the architecture and foundational components that will support the next generation of intelligent, interactive systems. Your decisions will influence the trajectory of multiple engineering teams and the product itself.

Requirements

  • 10+ years of professional software development experience
  • Experience designing and scaling distributed systems and real-time applications
  • Proven ability to lead technical strategy and cross-team architecture efforts
  • Proficiency in at least one backend systems language (e.g., Python, Go, Java)
  • Strong understanding of system design, performance optimization, and fault-tolerance
  • Engineers who have repeatedly solved difficult problems at scale, and who know how to make good technical decisions under uncertainty.

Nice To Haves

  • Experience bridging machine learning research and production environments
  • Background in building systems that operate in or interact with the physical world
  • Ability to influence engineering orgs and contribute to hiring and culture
  • Track record of mentoring senior and staff-level engineers

Responsibilities

  • Define and evolve the technical architecture across core systems and products
  • Provide hands-on technical leadership on strategic projects
  • Lead technical reviews and mentor engineers across all levels
  • Align engineering direction with product and ML research priorities
  • Identify and address scaling challenges before they become bottlenecks
  • Champion engineering excellence, reliability, and system sustainability

Benefits

  • Health and wellness benefits
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