Forward-Deployed / Applications Engineer

Forward-Deployed / Applications Engineer

Austin, Tx

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