Staff AI Engineer: Agentic AI & Mechanical Innovation

TE Connectivity Corporate Office Singapore

Santo Niño 1st

On-site

PHP 6,890,000 - 10,335,000

Full time

10 days ago

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Job summary

TE Connectivity Singapore is seeking a Staff R&D Scientist/Engineer to join our Corporate R&D Center. You will lead AI initiatives across data science, ML, and CAD-driven engineering to advance the AI Hub for TE.

Based at our Singapore HQ, you will mentor engineers, guide projects from problem definition to deployment, and collaborate with business units and external partners to deliver practical AI solutions that reduce cost and time to market.

Qualifications

PhD with 4+ years, or Master with 7+ years, or Bachelor with 10+ years in related field.
Proven track record leading technically complex R&D or engineering AI projects and delivering measurable impact.
Deep understanding of data science and artificial intelligence, including machine learning model development, validation, and optimization.
Hands‑on knowledge of generative AI and agentic AI concepts and technologies, such as LLMs, RAG, embeddings, tool calling, orchestration.
Broad understanding of multiphysics simulation (FEA) with CAD/CAE workflows; experience with AI-assisted design is desirable.
Strong programming skills in Python and ML frameworks; experience with software development practices.
Broad knowledge of manufacturing processes, materials, and product design.
Strong leadership and stakeholder management across functions and distributed teams.
Excellent communication to explain AI concepts and risks to engineers and senior stakeholders.

Responsibilities

Provide technical leadership for engineering AI initiatives, defining technology direction and roadmaps for the Singapore AI Hub.
Lead multiple cross‑functional projects applying data science and AI to new product development, design, and manufacturing workflows.
Develop AI/ML solutions using surrogate modeling, optimization, design tools, and data sources.
Develop agentic AI workflows and integration with CAD/CAE, simulation, PLM, and enterprise systems.
Establish evaluation approaches for model performance, robustness, safety, and business impact.
Collaborate with data science, mechanical engineering, materials, software, and other teams across TE sites.