Hardware Quality Engineer at Lambda

San Jose, California, United States

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$109,000 – $163,000Compensation

Mid-level (3 to 4 years)Experience Level

Full TimeJob Type

UnknownVisa

AI, Cloud Computing, Data CentersIndustries

Requirements

Experience working with hardware / data center / infrastructure systems
Strong at data analysis, statistics, and metrics (ability to turn raw data into insight)
Skilled in root cause analysis methods (5 Whys, fishbone, 8D, A3, etc.)
Comfortable managing cross-team communication, stakeholder expectations, and conflict resolution
Detail-oriented, process-driven, and quality-minded
Experience working with quality tools or QMS software (e.g. audit modules, ERP, defect tracking)
Communicate clearly in English (both written and verbal)

Responsibilities

Track, log, and manage all quality issues arising in the data center during deployment and production environment
Perform root cause analysis (RCA) for every failure (hardware, software, process)
Analyze production system metrics and quality data to detect trends, anomalies, or weak points
Improve turnaround time (TAT) for Return Merchandise Authorization (RMA) processes
Design, monitor, and drive corrective and preventive actions (CAPA)
Implement and verify containment actions to keep systems operational until permanent fixes are applied
Collaborate with operations, hardware, engineering, supply chain, and vendors to resolve quality issues
Capture and upload failure analysis (FA) reports and related data into Quality Management Systems (QMS)
Verify quality of spares (incoming and outgoing) to avoid repeat failures
Define and track quality KPIs / SLAs and report on quality performance to leadership
Oversee MRB (Material Review Board) inventory, rework, disposal decisions
Ensure the quality management system (QMS) is up to date, with necessary training rolled out
Work cross-functionally during hardware ramp, deployments, and upgrades to ensure quality gates

Skills

Root Cause Analysis

RCA

Failure Analysis

CAPA

RMA

QMS

Quality Management

Data Center Operations

Metrics Analysis

Supply Chain

Hardware Quality

Lambda

Cloud-based GPU services for AI training

About Lambda

Lambda Labs provides cloud-based services for artificial intelligence (AI) training and inference, focusing on large language models and generative AI. Their main product, the AI Developer Cloud, utilizes NVIDIA's GH200 Grace Hopper™ Superchip to deliver efficient and cost-effective GPU resources. Customers can access on-demand and reserved cloud GPUs, which are essential for processing large datasets quickly, with pricing starting at $1.99 per hour for NVIDIA H100 instances. Lambda Labs serves AI developers and companies needing extensive GPU deployments, offering competitive pricing and infrastructure ownership options through their Lambda Echelon service. Additionally, they provide Lambda Stack, a software solution that simplifies the installation and management of AI-related tools for over 50,000 machine learning teams. The goal of Lambda Labs is to support AI development by providing accessible and efficient cloud GPU services.

San Jose, CaliforniaHeadquarters

2012Year Founded

$372.6MTotal Funding

DEBTCompany Stage

AI & Machine LearningIndustries

201-500Employees

Risks

Nebius' holistic cloud platform challenges Lambda's market share in AI infrastructure.

AWS's 896-core instance may draw customers seeking high-performance cloud solutions.

Existential crisis in Hermes 3 model raises concerns about Lambda's AI model reliability.

Differentiation

Lambda offers cost-effective Inference API for AI model deployment without infrastructure maintenance.

Nvidia HGX H100 and Quantum-2 InfiniBand Clusters enhance Lambda's AI model training capabilities.

Lambda's Hermes 3 collaboration showcases advanced AI model development expertise.

Upsides

Inference API launch attracts enterprises seeking low-cost AI deployment solutions.

Nvidia HGX H100 clusters provide competitive edge in high-performance AI computing.

Strong AI cloud service growth indicates rising demand for Lambda's GPU offerings.

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