Principal AI Engineer

United States·Delawarelead

Machine Learning EngineerData

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Machine Learning EngineerData

We are seeking a Principal AI Engineer (computer engineering or electrical engineering background) with a strong product-development foundation to lead AI initiatives that accelerate time-to-market, improve engineering efficiency, and enable AI-driven product capabilities. This role spans five product lines within the IT Rack Infrastructure domain and supports AI efforts across sustaining engineering, New Product Development and Introduction (NPDI), and forward-looking blue-sky research. The ideal candidate combines deep technical credibility in real-world product development with a creative, research-oriented mindset to explore breakthrough AI methods and technologies that can redefine both engineering processes and products.

Responsibilities

~2 min read

→Lead and explore AI-related research and emerging AI-technologies focused on IT Rack Infrastructure products (1PH UPS, rack-based power distribution units, KVM and Serial devices, mechanical racks).
→Define the AI/ML architecture and technology roadmap for the IT Systems Business Unit.
→Identify high‑value AI use cases across single‑phase UPS, rack PDU, KVM/Serial, racks, and integrated solutions.
→Drive alignment of AI initiatives with product-line strategies and long-term business goals.
→Lead feasibility studies, prototypes, and proof-of-concept developments.
→Ensure AI/ML model’s robustness, performance, explainability, and lifecycle management.
→Lead AI initiatives for improving our NPDI process time-to-market and engineering efficiency.
→Work with engineering leaders across product lines to define technological needs and feasibility.
→Collaborate with embedded software and firmware teams to deploy models on constrained edge devices and real-time systems.
→Define requirements for data acquisition, signal conditioning, and model inference hardware/software.
→Establish practices for data collection, preprocessing, labeling, and governance.
→Implement Machine Learning Operations (MLOps) frameworks for continuous integration, testing, versioning, and monitoring of AI models.
→Develop prototype methods, models, and simulations to validate new ideas and support technology feasibility studies.
→Collaborate closely with cross-functional teams, including product development, engineering, and infrastructure teams, to ensure alignment of research objectives with overall company vision and market trends.
→Publish technical papers, file patents, and present findings to internal stakeholders and external audiences, contributing to the organization’s knowledge base and thought leadership.
→Identify and collaborate with external research institutions, industry partners, and academic organizations to leverage additional expertise and insights.
→Engage with standards bodies, and technology partners.
→Mentor and guide junior engineers, fostering a culture of curiosity, creativity, and technical excellence within the team.

Requirements

~1 min read

Master’s degree or PhD in Electrical Engineering, Computer Engineering, or related technical field.
10+ years of experience computer engineering or electrical engineering or advanced research in related fields.
5+ years of experience in AI/ML development, applied machine learning, embedded systems and advanced analytics.
Strong proficiency in:
- Python, ML frameworks (TensorFlow, PyTorch, scikit‑learn)
- Data engineering and MLOps tools
- Model validation, testing, and deployment practices
Experience deploying AI/ML models in embedded systems, cloud platforms, or distributed systems.
Ability to provide technical leadership and drive cross-functional initiatives.
Familiarity with IT rack systems and infrastructure components is a plus.
Demonstrated ability to conceptualize and validate transformative ideas in mechanical design and materials applications.
Strong communication and presentation skills.
Ability to work both independently and collaboratively, engaging with cross-functional teams and external partners effectively.

Experience in energy technologies, IoT, or industrial/embedded products.
Knowledge of digital twins, simulation environments, or control systems.
Familiarity with edge AI inference frameworks and optimized runtime environments.
Track record of patents, high quality research/publications, or technical presentations.
Having research articles in ICML, ICLR or L4DC conferences is a plus