Staff AI/Edge Engineer — Embedded, Client-Facing

Analog Devices, Inc.; San Jose (CA); Presencial; USD 198 000 - 272 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Discretionary bonus

Medical, vision and dental coverage

401k plan

Paid vacation and holidays

Sick time and other benefits

Resumo da oferta

Analog Devices, Inc. seeks a Staff Forward Deployed Engineer to embed with strategic customers and scale ADI’s AI and edge technology within real-world infrastructure.

You will write production-grade code, build data pipelines, APIs, and MLOps workflows, and frame customer problems into architectures across cross-functional teams.

Travel up to 10% is required; strong Python, on-site deployment, and excellent communication are essential for success.

Qualificações

• BS/MS in CS, EE, or related; 10+ years building production software.
• Strong Python plus experience with data pipelines, APIs, and MLOps/AI workflows.
• Comfort deploying and debugging on-site in complex enterprise environments.
• Excellent communication; able to translate customer needs into architecture.
• Willingness to travel and embed with strategic clients.

Responsabilidades

• Write production-grade code: data pipelines, APIs, and MLOps/AI workflows.
• Embed on-site with clients, partnering across CSG, VBU, SDP, and Edge AI teams.
• Frame customer problems and own the architecture that solves them.
• Drive rapid iteration and field learning to deliver measurable customer outcomes.
• Produce reusable, ADI-owned assets that create defensible wedges.

Conhecimentos

Degree in CS/EE or related

Python & ML pipelines

On-site deployment

Communication skills

Travel readiness

Formação académica

BS/MS in CS or EE

Descrição da oferta de emprego

Analog Devices, Inc. seeks a Staff Forward Deployed Engineer to embed with strategic customers and scale ADI’s AI and edge technology within real-world infrastructure.

You will write production-grade code, build data pipelines, APIs, and MLOps workflows, and frame customer problems into architectures across cross-functional teams.

Travel up to 10% is required; strong Python, on-site deployment, and excellent communication are essential for success.