Research AI Engineer: Agentic Platform & Evaluation

AIDAChip Inc; Seattle (WA); Hybrid; USD 180,000 - 240,000; Full time

Job summary

AIDAChip is the Alignment OS for AI-native silicon engineering teams — the first AI platform built for chip design teams, not just individuals. You will own the engine that turns expert agent designs into production agents and you will rigorously test performance to ensure reliability.

You will drive both platform engineering and evaluation research, partnering with chip-design experts to ship and measure systems that operate at scale with strong cost, latency, and reliability discipline.

Qualifications

• Experience shipping multi-agent/LLM systems to production.
• Strong backend engineering with scalability focus.
• Rigorous eval design and statistical testing.
• Ability to partner with domain experts and ship.

Responsibilities

• Build the agent factory (harness, packaging, tool wiring) so a new role agent ships in weeks, not months
• Design and ship orchestration agents (workflow coordination, verification orchestration, cross-discipline handoff)
• Hold the fleet efficiency bar: minimize turns/task and agent token overhead, keep critical paths sub-second
• Design the human-eval program with chip-design experts; build auto-evaluation that reaches parity with human judges
• Research agent consistency: variance across repeated runs, drift across model swaps
• Instrument the experience signals that gate the product: task completion, abandonment, HITL integrity
• Pair daily with chip-design domain experts: they define what each agent must do and how it's judged; you ship it and measure it

Skills

Multi-agent systems

LLM orchestration

Back-end engineering

Evaluation methodology

Production CI gates

Job description

Research AI Engineer: Agentic Platform & Evaluation

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; we replace that with a multiplayer AI layer that keeps intent, knowledge, and execution coherent from specification to tapeout.

This role owns two tightly coupled areas at once: building our agent fleet and proving it works. You'll drive both the platform engineering (the agent machinery) and the research science (evaluation and publication) — owning the machinery that turns a domain expert's agent design into a shipped production agent, while owning the rigorous testing that guarantees its performance.

Key responsibilities

• Build the agent factory (harness, packaging, tool wiring) so a new role agent ships in weeks, not months
• Design and ship orchestration agents (workflow coordination, verification orchestration, cross-discipline handoff)
• Hold the fleet efficiency bar: minimize turns/task and agent token overhead, keep critical paths sub-second
• Design the human-eval program with chip-design experts; build auto-evaluation that reaches parity with human judges
• Research agent consistency: variance across repeated runs, drift across model swaps
• Instrument the experience signals that gate the product: task completion, abandonment, HITL integrity
• Pair daily with chip-design domain experts: they define what each agent must do and how it's judged; you ship it and measure it

You may be a good fit if you have

• Shipped multi-agent / LLM-agent systems to production: tool-use harnesses, orchestration, durable long-running tasks
• Deep cost/latency/reliability discipline for LLM systems at scale, with strong backend engineering
• Rigor in eval methodology: inter-rater reliability, paired statistical testing, LLM-judge calibration
• Production engineering ability: evaluation work ships code into CI gates, not notebooks

Strong candidates may also have experience with

• Dealing with vendors and mass data collection systems
• A PhD (ML/NLP/IR or adjacent), an exceptional publication record, or an exceptional shipping-systems record

How we're different

• Evidence over vibes: every behavior ships with a witness; claims verified before asserted; systems fail closed and loud
• Measured, not narrated: a score not in the eval database didn't happen; benchmarks gate merges
• Agent leverage: each engineer directs fleets of AI agents, shipping at a multiple of typing speed while holding the quality bar
• Human-in-the-loop by design: agents propose; humans approve what matters
• Greenfield ownership: a whole North-Star workstream, not a slice of a slice — on a production platform with real enterprise silicon customers, not a demo