Manufacturing Engineer - Data Center Hardware

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Anthropic is building its own servers, racks, and accelerator systems with contract manufacturers and ODM partners, and deploying them across our fleet of data centers. We are seeking experienced engineers to own how well that hardware gets built. As a Product Engineer on the Manufacturing Operations team, you are the technical owner of a product at the factory. During development, you gather manufacturing, test, and yield concerns, get them fixed in the design before each build, and sign off that the product is ready to move to the next stage. Once a product is in mass production, you keep it healthy by delivering engineering changes with minimal disruption, qualifying new components and second-source manufacturers, and root-causing yield and field failures. This is a hands-on role with up to 50% travel to partner factories in the US and abroad. You should be as comfortable debugging a test failure on the line as you are presenting a corrective action plan to leadership. Representative projects Take a new accelerator rack from early evaluation to mass production at an ODM: audit the assembly and test process, set yield targets, confirm test coverage, and approve the transition. Scale manufacturing from one line to multiple and then to a second site or second manufacturer without yield or throughput regressions. Run a cross-functional root cause on field failures traced to a manufacturing gap, drive corrective action with the ODM/CM, and close the loop with the design itself when required. Lead an engineering change across manufacturers: BOM and PLM updates, cut-in planning, partner training, validation, and monitor fie

About The Role Own the end-to-end data-center hardware quality and reliability loop for OpenAI’s 3P infrastructure and 1P current and next-gen platforms. Turn field failures into quantified risk, fast containment, verified root cause, improved MQE/NPI and manufacturing-test coverage, accurate spares forecasts, and upstream changes that prevent recurrence. The first hire must combine practical hardware/system understanding, reliability engineering, data fluency, and cross-functional technical leadership at data-center scale. Key Responsibilities • Build and govern the field-quality data model across telemetry, tickets, RMA/repair, FA, firmware, configuration, supplier, and manufacturing genealogy. • Define AFR, ASR, DPPM, MTBF/MTTR, repeat-repair, NTF, repair-cycle-time, and forecast-versus-actual metrics with explicit denominators and uncertainty. • Provide fleet-level macro views and unit/FRU/cohort-level micro views; detect shifts and bound affected populations. • Lead systemic field-failure triage, containment, failure analysis, 8D/CAPA, risk assessment, corrective-action verification, and recurrence monitoring. • Develop cohort, life-data, reliability-growth, and spare-demand projections by product, FRU, supplier, configuration, geography, and age. • Partner with MQE and NPI to convert field mechanisms into manufacturing-test coverage, screening/stress profiles, diagnostics, control plans, DFR/DFS requirements, FMEA/FTA, mission profiles, FRU strategy, and qualification gates. • Close the loop by verifying whether upstream changes reduce field recurrence. • Define supplier/CM FA standards, field-data contracts, scorecards, escalation paths, and closure evidence. • Provide serviceability, TCO, and spares inputs without owning inventory execution or procurement. • Create concise executive decision packages: population at risk, exposure, confidence, options, cost/risk, and recommendation. • Run the cross-functional reliability council and, as the team grows, mentor

SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars. SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next. ABOUT THE ROLE: The Data Center Engineering team designs training and inference compute clusters from first principles, pushing thermal, electrical, and optical systems to the limit of physics. We operate as a highly cross disciplinary team comprised of mechanical, electrical, optical, and software engineers, all working to maximize the efficiency, reliability, and deployment speed of terrestrial computing to serve rapidly scaling demand. This team owns compute architecture, datacenter network design, rack builds, connectivity, in-house design of our own computers, and the manufacturing line that builds that compute. As a Rack Design Engineer, you will own the rack as a product: structure, power, cooling, service paths, and the physical integration of servers, switches, optics, and liquid cooling from first sketch through production rate. RESPONSIBILITIES: Own mechanical, structural, and integration design of high-density AI compute racks and associated chassis, frames, busbars, manifolds, and cable/fiber raceways Integrate compute, networking, power distribution, and liquid cooling into a buildable, serviceable, high-yield rack architecture Design for the in-house manufacturing

SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars. SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next. ABOUT THE ROLE: Determine true root cause of fleet hardware failures — component and system level — and drive fixes through vendors to the manufacturer. Turn "swap it again" into "vendor redesign / firmware fix / manufacturing escape found." RESPONSIBILITIES: Own named failure classes across GPU trays/baseboards, NIC/DPU, motherboard/PCIe switch, memory, power, thermal, cables/connectors, and rack-scale patterns. Run recurrence analysis and fleet-wide defect clustering; detect systemic patterns before they become fleet-scale loss. Build vendor technical escalation packages with evidence quality that forces action; partner with OEM/ODM/component manufacturers on firmware, bring-up, and manufacturing escapes through committed CAPA. Feed findings into RMA policy, spare strategy, and "do not reseat forever" stop-rules. Work the FA intake queue on rotation; keep queue age within SLA BASIC QUALIFICATIONS: Bachelor's degree in Systems Engineering, Electrical Engineering, Computer Science, or a related field (or equivalent experience). 2+ years of experience in hardware reliability engineering, preferably in high-performance computing or data center environments. Proven expertise in fir

About Graphcore Graphcore is a global leader in artificial intelligence computing systems. We design advanced semiconductors and data center hardware that provide the specialized processing power needed to advance AI while improving the efficiency required for broad adoption. As part of SoftBank Group, Graphcore belongs to a family of companies developing transformative technologies. Our AI Engineering Campus in Austin plays an important role in building the hardware platforms that support the next generation of AI systems. The Opportunity We are looking for a recent graduate or early-career engineer to join the Mech/Thermal Platform team as a Graduate Mechanical Engineer. You will contribute to the mechanical design, thermal validation, and verification of advanced AI hardware and data center systems. You will work with experienced mechanical, thermal, hardware, and systems engineers throughout the development lifecycle. The role combines 3D computer-aided design, prototype evaluation, lab testing, data analysis, troubleshooting, and clear engineering documentation. What You Will Do Create and update mechanical parts, assemblies, and drawings using Creo or comparable 3D CAD software. Take ownership of defined mechanical design tasks from requirements and concepts through detailed design, review, release, and verification. Apply mechanical design, materials, manufacturing, heat transfer, and thermodynamics principles to engineering decisions. Develop mechanical and thermal test plans and procedures for prototypes and development systems. Set up and operate laboratory equipment, collect accurate data, analyze results, and document conclusions. Evaluate prototypes and identify mechanical, thermal, assembly, or manufacturability issues. Support design verification, troubleshooting, root-cause analysis, and implementation of verified design improvements. Maintain accurate engineering documentation, including design notes, drawings, test procedures, results, and change r

About the Team OpenAI is building the infrastructure foundation for the next generation of AI. The Data Center Engineering team defines the strategy, reference architectures, technical requirements, and delivery standards for the large-scale data centers that support OpenAI research, products, and infrastructure partners. As a Data Center Infrastructure Electrical Engineer, you will help define, validate, and scale the electrical power systems that support high-density AI compute. You will translate evolving compute requirements into practical facility and rack-power architectures, evaluate new technologies and vendor solutions, and drive technical decisions across design, manufacturing validation, construction, commissioning, deployment, and operations. This role is best suited for a senior hands-on engineer with deep experience in mission-critical power systems, strong judgment under ambiguity, and the ability to connect facility infrastructure, hardware requirements, controls, telemetry, reliability, and operations. About the Role We are seeking a senior electrical infrastructure engineer to lead the development of reliable, scalable, and efficient power architectures for high-density, liquid-cooled AI data centers. The ideal candidate has strong practical experience with critical electrical systems at data centers or comparable industrial scale, including medium-voltage and low-voltage distribution, utility interfaces, backup power, UPS and battery systems, rack power delivery, grounding, protection, controls, and monitoring systems. You should be comfortable moving between long-range architecture, detailed engineering review, lab validation, vendor qualification, field deployment, and operational troubleshooting. Key Responsibilities Design and optimize electrical topologies and equipment strategies that reduce cost, accelerate schedules, improve efficiency, increase scalability, and maintain high reliability and maintainability. Review and develop basis-of-des