About Etched
Etched is building AI chips that are hard-coded for individual model architectures. Our first product (Sohu) only supports transformers, but has an order of magnitude more throughput and lower latency than a B200. With Etched ASICs, you can build products that would be impossible with GPUs, like real-time video generation models and extremely deep & parallel chain-of-thought reasoning agents.
Job Summary
We’re looking for a hands-on and detail-driven Installation Manager to own the deployment of Etched’s rack-based AI systems into customer data centers. You’ll be responsible for defining the installation process from scratch, documenting best practices, and coordinating all in-field deployments. This includes working closely with internal teams, third-party vendors, and customer site leads to ensure smooth, repeatable, and high-quality installations of our systems worldwide.
You’ll be the tip of the spear for customer-facing rack deployment—ensuring Etched systems are installed correctly, safely, and on schedule.
Key responsibilities
Develop and document the end-to-end process for installing Etched racks in data centers (power, networking, cooling, cabling, bring-up, validation)
Coordinate with internal engineering, product, and operations teams to ensure racks are installation-ready
Manage pre-installation planning, including site surveys, shipping schedules, customer communications, and hardware prep
Serve as the primary point of contact for all customer site installations—owning timelines, checklists, dependencies, and staffing
Select, train, and manage 3rd-party installation contractors or white-glove service providers as needed
Own post-installation signoff, rack activation, and documentation capture
Maintain an installation playbook that evolves with each deployment—capturing lessons learned and updating SOPs
Collaborate with support and logistics teams to manage issue escalations and coordinate replacement parts or re-visits
Track installation performance metrics (first-time-right rate, time to install, site issues) and drive continuous improvement
You may be a good fit if you have
5+ years experience in hardware deployment, field service engineering, or installation program management—ideally for servers, racks, or datacenter equipment
Strong understanding of datacenter infrastructure (rack power/cooling, structured cabling, PDUs, network uplinks, etc.)
Proven ability to define and document technical processes from scratch
Excellent coordination skills across technical, operational, and customer-facing functions
Able to travel to customer sites for installations and hands-on validation
Effective communicator with a calm, problem-solving mindset under pressure
Strong candidates may also have experience with
Experience deploying AI servers, GPU clusters, or custom hardware into hyperscaler or colocation environments
Familiarity with DCIM tools or datacenter deployment management systems
Background in mechanical, electrical, or systems engineering
Experience with field team training, site audits, or installation certifications
Benefits
Full medical, dental, and vision packages, with generous premium coverage
Housing subsidy of $2,000/month for those living within walking distance of the office
Daily lunch and dinner in our office
Relocation support for those moving to San Jose (Santana Row)
How we’re different
Etched believes in the Bitter Lesson. We think most of the progress in the AI field has come from using more FLOPs to train and run models, and the best way to get more FLOPs is to build model-specific hardware. Larger and larger training runs encourage companies to consolidate around fewer model architectures, which creates a market for single-model ASICs.
We are a fully in-person team in San Jose (Santana Row), and greatly value engineering skills. We do not have boundaries between engineering and research, and we expect all of our technical staff to contribute to both as needed.
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by burning the transformer architecture into our chips, we’re creating the world’s most powerful servers for transformer inference.
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