You will set the deployment-engineering strategy for cluster build-out , how racks are organized into pods, halls, and sites; how compute, network, power, and cooling systems interface at the rack boundary; and how deployment scope flows cleanly from hardware specification to facility delivery to a running cluster.

This role is focused on deployment engineering, not on datacenter network or systems design , your scope is making sure clusters land cleanly and predictably, not designing the fabrics or facilities themselves.

You will work across hardware, networking, facilities, supply chain, and construction to ensure that every generation of accelerator we deploy lands in a datacenter that is ready for it , on schedule, at full density, and with every piece of required infrastructure accounted for.

You will be the person who sees around corners: anticipating how next-generation rack designs will stress our facilities, where our deployment model will break at scale, and what needs to change now so that the next cluster turn-up is faster and more predictable than the last.

You will operate at the intersection of engineering strategy and execution discipline, partnering with internal research and systems teams, external developers, engineering firms, and OEM partners to deliver cluster capacity at the speed the frontier demands.

Responsibilities:

• Own cluster-level deployment strategy , define how AI compute clusters are organized across the floor, how racks interconnect, and how cluster topology requirements translate into facility and deployment scope across a portfolio of sites.

• Set rack interface standards spanning power, network, mechanical, thermal, and spatial domains, and ensure that every deployment includes the complete set of infrastructure required to bring a cluster online.

• Drive multi-threaded cluster bring-up programs across hardware, networking, power, and cooling , owning plans, dependencies, and critical paths from hardware specification through energization and turn-up.

• Partner with internal engineering teams , research, systems, networking, and hardware , to translate cluster requirements into deployable facility scope, and to derisk onboarding of new hardware platforms well ahead of delivery.

• Lead external partner execution with developers, engineering firms, OEMs, and construction teams, driving technical reviews, deviation management, and handoffs that keep deployments on schedule and within specification.

• Improve cluster turn-up reliability and repeatability , identify systemic gaps in deployment scope, tooling, and partner interfaces, and drive durable fixes that reduce time-to-serve for new capacity.

• Define and track deployment KPIs , cluster readiness, schedule adherence, scope completeness, time-to-first-packet , and use historical trends to forecast risk and inform capacity planning.

• Coordinate cross-functional readiness across supply chain, security, operations, and construction to ship production-ready compute capacity.

• Provide crisp executive visibility on deployment progress, tradeoffs, and risks across a portfolio of concurrent cluster programs.

• Design cluster interfaces for durability , define rack and cluster-level interfaces that remain robust across hardware generations, so that facility scope and deployment models do not need to be reinvented every time the underlying hardware changes.

• Build cluster layout and BOM tooling , create and maintain the tools, templates, and data models that turn cluster topology and rack specifications into accurate floor layouts, deployment sequences, and complete bills of materials, replacing one-off spreadsheets with repeatable, auditable workflows.

You may be a good fit if you:

• Have 10+ years of experience in hyperscale datacenter environments, with senior-level responsibility for cluster deployment, large-scale IT integration, or equivalent infrastructure programs.

• Have delivered AI, HPC, or high-density compute clusters at scale and developed a strong intuition for the constraints that govern cluster deployment , interconnect reach, adjacency, power density, and thermal limits.

• Can operate fluently across the boundary between IT hardware and facility infrastructure, and have set interface standards that held up across multiple hardware generations and sites.

• Have led cross-functional programs with both internal engineering teams and external developers, engineering firms, and OEM partners, and are effective at driving alignment across organizational levels.

• Combine strong systems thinking with execution discipline , comfortable zooming from cluster topology and portfolio strategy down to the specific interface detail that will otherwise become a field issue.

• Communicate clearly with technical and executive audiences, and can distill complex, multi-disciplinary programs into decisions and tradeoffs leadership can act on.

• Thrive in ambiguous, fast-moving environments where the hardware, the scale, and the requirements are all changing simultaneously.

• Hold a Bachelor's degree in Electrical Engineering, Mechanical Engineering, Computer Engineering, or equivalent practical experience.

Strong candidates may also:

• Have direct experience deploying leading-edge AI accelerator clusters at hyperscale.

• Have shaped reference designs, deployment standards, or cluster-level playbooks that were adopted across a fleet.

• Have experience working across multiple geographies and understand how regional codes, climate, utility constraints, and supply chains shape cluster-level decisions.

• Have partnered closely with hardware and system providers on long-term platform onboarding and bring-up.

• Have experience building the program mechanisms , roadmaps, milestones, risk registers, runbooks , that make delivery predictable at massive scale.

The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings (“OTE”) range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

Annual Salary: $320,000-$405,000 USD

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You will set the deployment-engineering strategy for cluster build-out , how racks are organized into pods, halls, and sites; how compute, network, power, and cooling systems interface at the rack boundary; and how deployment scope flows cleanly from hardware specification to facility delivery to a running cluster.

This role is focused on deployment engineering, not on datacenter network or systems design , your scope is making sure clusters land cleanly and predictably, not designing the fabrics or facilities themselves.

You will work across hardware, networking, facilities, supply chain, and construction to ensure that every generation of accelerator we deploy lands in a datacenter that is ready for it , on schedule, at full density, and with every piece of required infrastructure accounted for.

You will be the person who sees around corners: anticipating how next-generation rack designs will stress our facilities, where our deployment model will break at scale, and what needs to change now so that the next cluster turn-up is faster and more predictable than the last.

You will operate at the intersection of engineering strategy and execution discipline, partnering with internal research and systems teams, external developers, engineering firms, and OEM partners to deliver cluster capacity at the speed the frontier demands.

Responsibilities:

• Own cluster-level deployment strategy , define how AI compute clusters are organized across the floor, how racks interconnect, and how cluster topology requirements translate into facility and deployment scope across a portfolio of sites.

• Set rack interface standards spanning power, network, mechanical, thermal, and spatial domains, and ensure that every deployment includes the complete set of infrastructure required to bring a cluster online.

• Drive multi-threaded cluster bring-up programs across hardware, networking, power, and cooling , owning plans, dependencies, and critical paths from hardware specification through energization and turn-up.

• Partner with internal engineering teams , research, systems, networking, and hardware , to translate cluster requirements into deployable facility scope, and to derisk onboarding of new hardware platforms well ahead of delivery.

• Lead external partner execution with developers, engineering firms, OEMs, and construction teams, driving technical reviews, deviation management, and handoffs that keep deployments on schedule and within specification.

• Improve cluster turn-up reliability and repeatability , identify systemic gaps in deployment scope, tooling, and partner interfaces, and drive durable fixes that reduce time-to-serve for new capacity.

• Define and track deployment KPIs , cluster readiness, schedule adherence, scope completeness, time-to-first-packet , and use historical trends to forecast risk and inform capacity planning.

• Coordinate cross-functional readiness across supply chain, security, operations, and construction to ship production-ready compute capacity.

• Provide crisp executive visibility on deployment progress, tradeoffs, and risks across a portfolio of concurrent cluster programs.

• Design cluster interfaces for durability , define rack and cluster-level interfaces that remain robust across hardware generations, so that facility scope and deployment models do not need to be reinvented every time the underlying hardware changes.

• Build cluster layout and BOM tooling , create and maintain the tools, templates, and data models that turn cluster topology and rack specifications into accurate floor layouts, deployment sequences, and complete bills of materials, replacing one-off spreadsheets with repeatable, auditable workflows.

You may be a good fit if you:

• Have 10+ years of experience in hyperscale datacenter environments, with senior-level responsibility for cluster deployment, large-scale IT integration, or equivalent infrastructure programs.

• Have delivered AI, HPC, or high-density compute clusters at scale and developed a strong intuition for the constraints that govern cluster deployment , interconnect reach, adjacency, power density, and thermal limits.

• Can operate fluently across the boundary between IT hardware and facility infrastructure, and have set interface standards that held up across multiple hardware generations and sites.

• Have led cross-functional programs with both internal engineering teams and external developers, engineering firms, and OEM partners, and are effective at driving alignment across organizational levels.

• Combine strong systems thinking with execution discipline , comfortable zooming from cluster topology and portfolio strategy down to the specific interface detail that will otherwise become a field issue.

• Communicate clearly with technical and executive audiences, and can distill complex, multi-disciplinary programs into decisions and tradeoffs leadership can act on.

• Thrive in ambiguous, fast-moving environments where the hardware, the scale, and the requirements are all changing simultaneously.

• Hold a Bachelor's degree in Electrical Engineering, Mechanical Engineering, Computer Engineering, or equivalent practical experience.

Strong candidates may also:

• Have direct experience deploying leading-edge AI accelerator clusters at hyperscale.

• Have shaped reference designs, deployment standards, or cluster-level playbooks that were adopted across a fleet.

• Have experience working across multiple geographies and understand how regional codes, climate, utility constraints, and supply chains shape cluster-level decisions.

• Have partnered closely with hardware and system providers on long-term platform onboarding and bring-up.

• Have experience building the program mechanisms , roadmaps, milestones, risk registers, runbooks , that make delivery predictable at massive scale.

The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings (“OTE”) range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

Annual Salary: $320,000-$405,000 USD

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Key responsibilities include:

• Design, implement, and document custom Python applications using PyAnsys and other libraries
• Capture and implement industry best practices related to modeling and simulation of engineered products
• Create compelling technical marketing content to showcase outcomes of your developed frameworks and examples
• Present results and progress regularly to Synopsys teams
• Collaborate with Customer Excellence teams for technical discussions and to gather requirements

Requirements include:

• Currently enrolled in a Bachelor's or Master's program in engineering or a related technical discipline at an accredited university in the United States or Canada with a minimum 3.0 GPA
• Strong proficiency in Python
• Experience with package management (Poetry/Pip), version control (GitHub), PyAnsys, and web applications is highly desirable
• Experience with NVIDIA Omniverse (Kit, USD, or Create) and knowledge of USD (Universal Scene Description) are highly desirable
• Exposure to Ansys CFD software, CAD packages, and a general understanding of IT hardware/system integration is highly desirable
• Excellent written and verbal communication skills in English
• Self-motivated with strong problem-solving, communication, and time management skills

Key program facts include:

• Program length: 3 months
• Location: Canonsburg, Pennsylvania; Evanston, Illinois; or Waterloo, Ontario
• Full-time/part-time: Full-time
• Start date: May or June 2026 (flexible based on academic calendar)

The base salary range across the U.S. for this role is between $32.00-$48.00 per hour. In addition, this role may be eligible for an annual bonus, equity, and other discretionary bonuses. Synopsys offers comprehensive health, wellness, and financial benefits as part of a competitive total rewards package.

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