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Etched is building custom hardware for frontier AI inference, co-designing chips, racks, software, and manufacturing to deliver best-in-class throughput and latency. The company is Series A-backed by top-tier investors and staffed by leading engineers.
You will serve as PCBA Design for Manufacturing (DFM) Engineer, owning the manufacturability of next-generation hardware from early design through production release. This is a senior individual contributor role that bridges design and manufacturing, ensuring reliable, scalable production across internal SMT operations and external contract manufacturers.
Key responsibilities include:
- Lead PCBA DFM reviews from concept through production release, evaluating component placement, footprints, spacing, solderability, assembly access, inspection coverage, and rework feasibility.
- Drive manufacturing risks to closure by translating review findings into actionable design changes, establishing owners and deadlines, and verifying implementation.
- Establish and maintain DFM rules, checklists, and review milestones reflecting capabilities of internal and external manufacturing partners.
- Maintain comprehensive engineering documentation including review records, issue trackers, design decisions, and lessons learned.
- Support manufacturing readiness by identifying assembly challenges early and preparing products for repeatable production at volume.
- Collaborate with manufacturing partners to evaluate DFM feedback and align requirements across operations.
- Investigate design-related assembly issues during prototype and NPI builds and incorporate findings into future revisions.
- Improve yield and manufacturing cost using build results, inspection data, and failure analysis to reduce defects, respins, scrap, and rework.
- Partner with Test Engineering to address test access, design-for-test requirements, and inspection limitations.
- Apply AI and automation tools to develop technical reports, summarize findings, and create lightweight applications for documentation and data analysis.
The role is fully in-person at the San Jose office (Santana Row). Etched values engineering skills and expects technical staff to contribute across disciplines.
REQUIREMENTS (Must-have):
- Degree in electrical, mechanical, manufacturing, or materials engineering, or equivalent practical experience.
- Direct experience in PCBA DFM, manufacturing engineering, or hardware NPI, with involvement in moving complex electronics from design into production.
- Strong understanding of SMT assembly processes: stencil design, solder paste printing, component placement, reflow, inspection, and rework.
- Experience reviewing PCB layouts, component footprints, assembly drawings, bills of materials, and manufacturing release packages.
- Knowledge of manufacturability challenges in high-density assemblies, fine-pitch components, and large BGA packages.
- Ability to distinguish design-change issues from process-development issues.
- Strong documentation and organizational skills, including revision history management, action-item tracking, and clear technical reporting.
- Practical experience using AI tools for technical reporting, analysis, automation, or application development, with sound judgment reviewing outputs.
- Direct experience working with design engineers and contract manufacturers to resolve technical issues.
- Familiarity with IPC-A-610, J-STD-001, and relevant PCB design and fabrication standards.
- Strong ownership, clear communication, and ability to prioritize manufacturing risks across concurrent programs.
NICE-TO-HAVE:
- Experience with high-performance compute, accelerator, server, or networking hardware.
- High-layer-count PCBs, HDI structures, via-in-pad designs, board or package warpage.
- Thermally demanding or high-power assemblies.
- Establishing DFM guidelines and formal design review processes.
- Hands-on solder paste inspection, AOI, X-ray inspection, and assembly failure analysis.
- Supporting both internal SMT operations and external manufacturing partners.
- Python, SQL, or similar tools for engineering data analysis and automation.
- Machine learning applications in manufacturing (defect classification, anomaly detection, yield prediction, automated inspection).
