Build and ship production software that automates quality control, factory scheduling, integrator scoring, and corrective actions for AI supercomputer manufacturing. Embed with domain experts on factory floors to turn their judgment into self-improving graph-based systems; requires production coding in Go/Python/TS, LLM/agent experience, and strong product taste for industrial workflows.
150k – 275k/yr
On-siteFullstack Engineering
About the role
Responsibilities
Build quality control as software: generate inspection and test plans, first-article inspections, and factory acceptance test procedures per asset from design revision; verify results against spec automatically, enforce hold points, and open nonconformance reports with evidence on failure.
Ship a self-replanning factory schedule: derive work orders, sequencing, and kitting from demand and BOM; track live throughput and yield per line and integrator; recompute and flag delivery impact on test failures or shipment slips.
Score third-party integrators continuously on defect density, first-pass test rate, and corrective-action speed to drive qualification, load balancing, and awards.
Close the corrective-action loop: trace defects from commissioning or field back to design revision, factory, and bench; automatically incorporate fixes into next run's inspection plan.
Embed with modular design engineers, quality managers, and factory operators in factories and at integrator facilities; model BOM lineage, test history, revision, and acceptance evidence in a graph to answer "can we ship it" via query.
Requirements
Shipped production code in Go, Python, or TypeScript; able to pick up whatever language the problem demands.
Built real features on LLM APIs (OpenAI, Anthropic, or open-weight models), MCP servers, and agentic frameworks.
Work daily with AI coding tools like Claude Code and Cursor; able to get agents doing useful work autonomously.
Identify problems, design solutions, and ship without waiting for direction or approval.
Moved fast under deadline while leaving extensible foundations for other engineers.
Sat with non-technical domain experts, turned their judgment into working systems, and drove adoption on a production line.
Product taste demonstrated in shipped interfaces that feel obvious to floor users and workflows that match real work.
Nice-to-Haves
Experience with MES or PLM systems.
Knowledge of inspection and test plans, factory acceptance testing.
Experience with NCR and corrective-action workflows.
BOM management.
Scheduling and optimization problems.
Compensation & Benefits
Cash compensation range: $150,000 - $275,000 (final offers may vary based on geography, experience, expertise, licenses, and other factors).
Outstanding candidates may receive adjusted terms plus meaningful equity.
Build and ship a live deployment knowledge graph and agentic software systems that automate AI supercomputer site bring-up, contractor workflows, QA inspections, and as-builts. Forward-deploy alongside field engineers on live construction sites to turn their domain expertise into structured, queryable data models and autonomous planning tools. Requires production experience with Go/Python/TypeScript, LLM APIs, and agentic coding tools.
150k – 275k/yr
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