Principal Engineer, Resource Optimization and Fleet Logic
for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real content. We need to parse the posting.
Given text includes:
- Application window info: open until at least September 29, 2026. This opportunity will remain online based on business needs which may be before or after the specified date. Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Thornton, CO, USA; Atlanta, GA, USA; Austin, TX, USA; Sunnyvale, CA, USA.
- Minimum qualifications: Bachelor's degree in Computer Science or similar technical field, or equivalent practical experience. 15 years of experience as a software engineer. Experience delivering large-scale capacity planning, IaaS/PaaS solutions, or fleet management systems.
- Preferred qualifications: Master's degree or PhD in Computer Science or a field related (e.g., Networking or Security Systems). Experience architecting, leading, and delivering large-scale capacity planning, combinatorial optimization, fleet management, or distributed infrastructure transformations from concept to deployment. Deep understanding of modern AI/ML infrastructure demands (TPU/GPU topologies, accelerators) with the ability to integrate AI-driven solutions. Ability to influence and lead without direct authority, building strong cross-organizational relationships across disparate teams (e.g., Hardware, Software, Supply Chain, and Product Management). Exceptional communication skills, and ability to articulate complex mathematical, economic, and architectural concepts to engineering leaders and executive business stakeholders.
- About The Job: description about Google runs largest computational fleets, etc.
- Pay: "Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $307000 - $427000 (USD) + 30% bonus target + equity + benefits"
- Responsibilities: bullet list after "Responsibilities Learn more about benefits at Google ." Actually there is a line: "Responsibilities Learn more about benefits at Google . Lead the design and evolution of next generation global fleet planning, defining a multi-year engineering roadmap to deliver 10x more data center capacity with ML, compute & storage resources. Leverage deep technical knowledge across the data center stack, spanning ML hardware (TPUs/GPUs), general compute, power, cooling, physical space, supply chain workflows, and network topology to model, forecast, and dynamically reconfigure fleet resources. Partner across AI & Infrastructure organizations to define a unified capacity management product suite meeting AI training and inference for internal and external customers. Integrate cutting-edge AI/ML, operations research, and advanced mathematical optimization into capacity planning workflows to compress capacity cycles, eliminate stranded capacity, and maximize data center utilization. Collaborate with academic institutions and industry pioneers to anticipate technology trends."
- Benefits: Not explicitly listed besides mention of equity + benefits in pay line. There's a line "Learn more about benefits at Google ." but no explicit benefits list. So we should not create a Benefits section unless there is real specific content. The posting does not list benefits details; only mentions equity + benefits generically. Probably we should not create a Benefits section because no specific benefits listed. However the pay line includes "+ equity + benefits". That's not a detailed list. According to rules: never create Pay/Schedule/Benefits sections unless the posting actually states them. Pay is stated (salary range). So we include Pay section. Benefits: not specific, so omit. Schedule: not mentioned, omit.
- Skills: Not explicitly listed as a separate section; but we could infer from qualifications. However we should not invent a Skills section unless there is explicit content. The posting does not have a "Skills" heading. So we omit.
- Requirements: Could be Minimum qualifications. We'll treat Minimum qualifications as Requirements section? The instruction: use headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We have About the role (About The Job). Responsibilities present. Requirements: we have Minimum qualifications. Qualifications: we have Preferred qualifications. Could combine both? But we need to follow instruction: include a section only when posting has real, specific content for it. So we can have Requirements (minimum qualifications) and Qualifications (preferred qualifications). Both have content.
- About the role: we have About The Job paragraph.
- Responsibilities: we have list.
- Pay: we have salary range.
We need to output HTML fragment only, no /. Use for each section heading. Use - for any list of duties/requirements/benefits. Use
for prose.
We need to preserve all substantive information; do not invent or summarize away detail. Remove boilerplate: cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. So we need to strip out the EEO statement, accommodation line, etc. Also remove "Learn more about benefits at Google ." maybe it's part of responsibilities line? Actually it's "Responsibilities Learn more about benefits at Google . Lead the design..." That seems like a stray phrase. We should remove "Learn more about benefits at Google ." as it's likely a link placeholder. So responsibilities start after that.
Also remove "Individual pay is determined by factors including job-related skills, experience, and relevant education or training." That's part of pay description; we keep that as prose in Pay section.
We need to keep location info? The note about preferred working location: "By applying to this position you will have an opportunity to share your preferred working location from the following: Thornton, CO, USA; Atlanta, GA, USA; Austin, TX, USA; Sunnyvale, CA, USA." This is substantive info about location. Where to put? Could be part of About the role or maybe a separate section like Location? Not in allowed headings. Could fold into About the role paragraph. Since we cannot create new headings not in list, we should incorporate location info into About the role paragraph (or maybe as a
under About the role). The instructions: "Do NOT repeat the job title as a heading (the page already shows it) and do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short
or drop it." Location is not minor metadata? It's location preference; could be considered minor metadata. We can fold into a short
under About the role. Probably best to add a paragraph after the About the role description.
Also application window info: "The application window will be open until at least September 29, 2026. This opportunity will remain online based on business needs which may be before or after the specified date." That's also metadata; we can fold into a short
under About the role or maybe as a separate paragraph. We'll include it in About the role section as a paragraph.
Now we need to ensure we don't include boilerplate like EEO statement, accommodation line, etc.
Let's extract sections:
About the role (About The Job) content:
Paragraph: "Google runs one of the largest computational fleets in the world, with compute, storage, networking and dedicated accelerators spread across all continents. As Principal Engineer, you will own the architectural outlook and end-to-end technical strategy for our global capacity planning ecosystem. You will serve as the primary technical authority for a challenge, knitting together complex planning constraints, optimization scenarios, and machine deployment workflows to get optimal capacity online in the shortest time. As the Principal Engineer, Resource Optimization and Fleet Logic, you will pioneer the technological vision for Google’s planetary-scale capacity planning and fleet optimization architecture. You will define the multi-year architectural path to orchestrate data center capacity planning, placement, supply planning, and fleet reconfiguration for ML, compute & storage demand —ultimately empowering our largest, most demanding internal and external AI/ML customers. Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems."
Add location and application window info as separate short paragraphs maybe.
Responsibilities: list items:
- Lead the design and evolution of next generation global fleet planning, defining a multi-year engineering roadmap to deliver 10x more data center capacity with ML, compute & storage resources.
- Leverage deep technical knowledge across the data center stack, spanning ML hardware (TPUs/GPUs), general compute, power, cooling, physical space, supply chain workflows, and network topology to model, forecast, and dynamically reconfigure fleet resources.
- Partner across AI & Infrastructure organizations to define a unified capacity management product suite meeting AI training and inference for internal and external customers.
- Integrate cutting-edge AI/ML, operations research, and advanced mathematical optimization into capacity planning workflows to compress capacity cycles, eliminate stranded capacity, and maximize data center utilization.
- Collaborate with academic institutions and industry pioneers to anticipate technology trends.
We need to output as
- each.
Requirements (Minimum qualifications):
- Bachelor's degree in Computer Science or similar technical field, or equivalent practical experience.
- 15 years of experience as a software engineer.
- Experience delivering large-scale capacity planning, IaaS/PaaS solutions, or fleet management systems.
Qualifications (Preferred qualifications):
- Master's degree or PhD in Computer Science or a field related (e.g., Networking or Security Systems).
- Experience architecting, leading, and delivering large-scale capacity planning, combinatorial optimization, fleet management, or distributed infrastructure transformations from concept to deployment.
- Deep understanding of modern AI/ML infrastructure demands (TPU/GPU topologies, accelerators) with the ability to integrate AI-driven solutions.
- Ability to influence and lead without direct authority, building strong cross-organizational relationships across disparate teams (e.g., Hardware, Software, Supply Chain, and Product Management).
- Exceptional communication skills, and ability to articulate complex mathematical, economic, and architectural concepts to engineering leaders and executive business stakeholders.
Pay: prose: "Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $307000 - $427000 (USD) + 30% bonus target + equity + benefits"
We should not include the "Learn more about benefits at Google ." phrase.
Now output HTML fragment:
About the role
Google runs one of the largest computational fleets in the world, with compute, storage, networking and dedicated accelerators spread across all continents. As Principal Engineer, you will own the architectural outlook and end-to-end technical strategy for our global capacity planning ecosystem. You will serve as the primary technical authority for a challenge, knitting together complex planning constraints, optimization scenarios, and machine deployment workflows to get optimal capacity online in the shortest time. As the Principal Engineer, Resource Optimization and Fleet Logic, you will pioneer the technological vision for Google’s planetary-scale capacity planning and fleet optimization architecture. You will define the multi-year architectural path to orchestrate data center capacity planning, placement, supply planning, and fleet reconfiguration for ML, compute & storage demand —ultimately empowering our largest, most demanding internal and external AI/ML customers. Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.
Application window: open until at least September 29, 2026; may remain online based on business needs.
Preferred working locations: Thornton, CO, USA; Atlanta, GA, USA; Austin, TX, USA; Sunnyvale, CA, USA.
Responsibilities
- Lead the design and evolution of next generation global fleet planning, defining a multi-year engineering roadmap to deliver 10x more data center capacity with ML, compute & storage
- for any list of duties/requirements/benefits. Use
for prose. We need to preserve all substantive information; do not invent or summarize away detail. Remove boilerplate: cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. So we need to strip out the EEO statement, accommodation line, etc. Also remove "Learn more about benefits at Google ." maybe it's part of responsibilities line? Actually it's "Responsibilities Learn more about benefits at Google . Lead the design..." That seems like a stray phrase. We should remove "Learn more about benefits at Google ." as it's likely a link placeholder. So responsibilities start after that. Also remove "Individual pay is determined by factors including job-related skills, experience, and relevant education or training." That's part of pay description; we keep that as prose in Pay section. We need to keep location info? The note about preferred working location: "By applying to this position you will have an opportunity to share your preferred working location from the following: Thornton, CO, USA; Atlanta, GA, USA; Austin, TX, USA; Sunnyvale, CA, USA." This is substantive info about location. Where to put? Could be part of About the role or maybe a separate section like Location? Not in allowed headings. Could fold into About the role paragraph. Since we cannot create new headings not in list, we should incorporate location info into About the role paragraph (or maybe as a
under About the role). The instructions: "Do NOT repeat the job title as a heading (the page already shows it) and do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short
or drop it." Location is not minor metadata? It's location preference; could be considered minor metadata. We can fold into a short
under About the role. Probably best to add a paragraph after the About the role description. Also application window info: "The application window will be open until at least September 29, 2026. This opportunity will remain online based on business needs which may be before or after the specified date." That's also metadata; we can fold into a short
under About the role or maybe as a separate paragraph. We'll include it in About the role section as a paragraph. Now we need to ensure we don't include boilerplate like EEO statement, accommodation line, etc. Let's extract sections: About the role (About The Job) content: Paragraph: "Google runs one of the largest computational fleets in the world, with compute, storage, networking and dedicated accelerators spread across all continents. As Principal Engineer, you will own the architectural outlook and end-to-end technical strategy for our global capacity planning ecosystem. You will serve as the primary technical authority for a challenge, knitting together complex planning constraints, optimization scenarios, and machine deployment workflows to get optimal capacity online in the shortest time. As the Principal Engineer, Resource Optimization and Fleet Logic, you will pioneer the technological vision for Google’s planetary-scale capacity planning and fleet optimization architecture. You will define the multi-year architectural path to orchestrate data center capacity planning, placement, supply planning, and fleet reconfiguration for ML, compute & storage demand —ultimately empowering our largest, most demanding internal and external AI/ML customers. Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems." Add location and application window info as separate short paragraphs maybe. Responsibilities: list items: - Lead the design and evolution of next generation global fleet planning, defining a multi-year engineering roadmap to deliver 10x more data center capacity with ML, compute & storage resources. - Leverage deep technical knowledge across the data center stack, spanning ML hardware (TPUs/GPUs), general compute, power, cooling, physical space, supply chain workflows, and network topology to model, forecast, and dynamically reconfigure fleet resources. - Partner across AI & Infrastructure organizations to define a unified capacity management product suite meeting AI training and inference for internal and external customers. - Integrate cutting-edge AI/ML, operations research, and advanced mathematical optimization into capacity planning workflows to compress capacity cycles, eliminate stranded capacity, and maximize data center utilization. - Collaborate with academic institutions and industry pioneers to anticipate technology trends. We need to output as
- each.
Requirements (Minimum qualifications):
- Bachelor's degree in Computer Science or similar technical field, or equivalent practical experience.
- 15 years of experience as a software engineer.
- Experience delivering large-scale capacity planning, IaaS/PaaS solutions, or fleet management systems.
Qualifications (Preferred qualifications):
- Master's degree or PhD in Computer Science or a field related (e.g., Networking or Security Systems).
- Experience architecting, leading, and delivering large-scale capacity planning, combinatorial optimization, fleet management, or distributed infrastructure transformations from concept to deployment.
- Deep understanding of modern AI/ML infrastructure demands (TPU/GPU topologies, accelerators) with the ability to integrate AI-driven solutions.
- Ability to influence and lead without direct authority, building strong cross-organizational relationships across disparate teams (e.g., Hardware, Software, Supply Chain, and Product Management).
- Exceptional communication skills, and ability to articulate complex mathematical, economic, and architectural concepts to engineering leaders and executive business stakeholders.
Pay: prose: "Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $307000 - $427000 (USD) + 30% bonus target + equity + benefits"
We should not include the "Learn more about benefits at Google ." phrase.
Now output HTML fragment:
About the role
Google runs one of the largest computational fleets in the world, with compute, storage, networking and dedicated accelerators spread across all continents. As Principal Engineer, you will own the architectural outlook and end-to-end technical strategy for our global capacity planning ecosystem. You will serve as the primary technical authority for a challenge, knitting together complex planning constraints, optimization scenarios, and machine deployment workflows to get optimal capacity online in the shortest time. As the Principal Engineer, Resource Optimization and Fleet Logic, you will pioneer the technological vision for Google’s planetary-scale capacity planning and fleet optimization architecture. You will define the multi-year architectural path to orchestrate data center capacity planning, placement, supply planning, and fleet reconfiguration for ML, compute & storage demand —ultimately empowering our largest, most demanding internal and external AI/ML customers. Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.
Application window: open until at least September 29, 2026; may remain online based on business needs.
Preferred working locations: Thornton, CO, USA; Atlanta, GA, USA; Austin, TX, USA; Sunnyvale, CA, USA.
Responsibilities
- Lead the design and evolution of next generation global fleet planning, defining a multi-year engineering roadmap to deliver 10x more data center capacity with ML, compute & storage
- each.
Requirements (Minimum qualifications):
- Bachelor's degree in Computer Science or similar technical field, or equivalent practical experience.
- 15 years of experience as a software engineer.
- Experience delivering large-scale capacity planning, IaaS/PaaS solutions, or fleet management systems.
Qualifications (Preferred qualifications):
- Master's degree or PhD in Computer Science or a field related (e.g., Networking or Security Systems).
- Experience architecting, leading, and delivering large-scale capacity planning, combinatorial optimization, fleet management, or distributed infrastructure transformations from concept to deployment.
- Deep understanding of modern AI/ML infrastructure demands (TPU/GPU topologies, accelerators) with the ability to integrate AI-driven solutions.
- Ability to influence and lead without direct authority, building strong cross-organizational relationships across disparate teams (e.g., Hardware, Software, Supply Chain, and Product Management).
- Exceptional communication skills, and ability to articulate complex mathematical, economic, and architectural concepts to engineering leaders and executive business stakeholders.
Pay: prose: "Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $307000 - $427000 (USD) + 30% bonus target + equity + benefits"
We should not include the "Learn more about benefits at Google ." phrase.
Now output HTML fragment: