Field Applications Engineer - AI Chip Design Deployments

AIDAChip Inc; San Francisco (CA); Hybrid; USD 120,000 - 180,000; Full time

Job summary

AIDAChip Inc is seeking a Forward-Deployed Applications Engineer in the SF Bay Area to help customers deploy the AI-native silicon platform. You will work closely with design teams, configure methodologies, and ensure success on real designs in customer environments.

You will be the trusted liaison between customers and product, translating complex design flows into actionable outcomes while building strong relationships with senior engineers and driving value from pilot to expansion.

Qualifications

• Deep Workflow / EDA / chip-design fluency, knowledge of design flows from architecture to RTL-to-signoff.
• Customer-facing technical experience in applications/field engineering or similar roles.
• Genuine hands-on ability to configure, script, debug and unblock technical issues.
• Excellent communication to build trust with senior engineers and translate between customer and product.

Responsibilities

• Customer deployments end to end: onboard design teams, configure the platform to their methodology, ensure success on real designs.
• Maintain trusted-advisor relationship inside the customer's environment, speaking their Design and EDA flows.
• Provide structured feedback to product with signals on what is useful and what is missing.
• Lead Pilot to expansion, document wins, and support conversion.
• Own the customer relationship; collaborate with domain owners and share methodology playbooks.

Skills

Workflow fluency

EDA/chip-design fluency

Customer-facing experience

Hands-on problem solving

Strong communication

Job description

Forward-Deployed / Applications Engineer

SF Bay Area, CA

AIDAChip is the Alignment OS for AI-native silicon engineering teams — the first AI platform built for chip design teams, not just individuals. Chip design today is fragmented across disciplines, tools, and handoffs, so intent gets lost and work drifts. We're the shared nervous system that keeps intent, knowledge, and execution aligned from specification to tapeout.

The Role

Key responsibilities

• Customer deployments end to end — onboard design teams, configure the platform to their methodology, and make them successful on real designs
• The trusted-advisor relationship inside the customer's environment — you speak their Design and EDA flows and earn senior engineers' confidence
• The feedback loop into product — structured signal on what's useful, what's missing, and what "value" looks like to a design team
• Pilot → expansion — prove value, document wins, and support conversion
• Partnership with our domain owners — you own the customer relationship; they own the discipline depth, and you hand each other the methodology playbooks

You may be a good fit if you have

• Deep Workflow / EDA / chip-design fluency — you've worked in or alongside real design teams and know the flow (Architecture, analog/custom, and RTL-to-signoff)
• Customer-facing technical experience — applications / field engineering, solutions, forward-deployed, or a designer who loves working directly with customers
• Genuine hands-on ability — you configure, script, debug, and unblock, not just advise
• Excellent communication — you build trust with senior engineers and translate between customer and product

Strong candidates may also have experience with

• Both digital and analog / mixed-signal exposure
• Startup, early-customer, or design-partner environments
• AI / agentic tooling

How we're different

• Evidence over vibes: nothing is "done" until it's proven — results carry their proof, claims are verified before they're asserted, and failures surface loudly instead of slipping through
• Measured, not narrated: if it isn't measured, it didn't happen — we decide on data in a shared system of record, not on how good the update sounded
• Agent leverage: every person directs a fleet of AI teammates and delivers at a multiple of what a lone expert could, without lowering the bar
• Human-in-the-loop by design: the AI proposes; a human owns the calls that matter — judgment stays with the expert
• Greenfield ownership: you own a whole problem end to end — a real workstream on a production platform serving enterprise silicon customers, not a demo