AI Field Engineer / Forward Deployed Engineer (FDE)
Qureos · New York, NY · 2 wk ago
On-siteEngineering$176k–$228k/yrFull-time
We need to output HTML fragment with sections: headings for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. We need to parse the posting.
Given content:
Location: New York, NY / San Mateo, CA / Remote, USA
Employment Type: Full-time
Experience: 3–10 years
Compensation: $176K–$228K base | $220K–$285K OTE + meaningful equity
Visa: H-1B transfers and TN visas sponsored; O-1 considered case-by-case
About The Role: paragraph.
What You’ll Do: list of duties.
What We’re Looking For: description of backgrounds and required.
Required: bullet list of requirements.
Technical Experience: list of relevant tech.
Why Join?: bullet list of benefits.
We need to map to sections: About the role (the "About The Role" paragraph). Responsibilities (What You’ll Do). Requirements/Qualifications/Skills: we have "What We’re Looking For" (backgrounds) and "Required" (specific requirements) and "Technical Experience" (skills). Could combine into Requirements and Skills sections. But we need to avoid thin sections. Perhaps combine Requirements and Skills into one "Requirements" section with sublists? But spec says use
for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. We need to parse the posting.
Given content:
Location: New York, NY / San Mateo, CA / Remote, USA
Employment Type: Full-time
Experience: 3–10 years
Compensation: $176K–$228K base | $220K–$285K OTE + meaningful equity
Visa: H-1B transfers and TN visas sponsored; O-1 considered case-by-case
About The Role: paragraph.
What You’ll Do: list of duties.
What We’re Looking For: description of backgrounds and required.
Required: bullet list of requirements.
Technical Experience: list of relevant tech.
Why Join?: bullet list of benefits.
We need to map to sections: About the role (the "About The Role" paragraph). Responsibilities (What You’ll Do). Requirements/Qualifications/Skills: we have "What We’re Looking For" (backgrounds) and "Required" (specific requirements) and "Technical Experience" (skills). Could combine into Requirements and Skills sections. But we need to avoid thin sections. Perhaps combine Requirements and Skills into one "Requirements" section with sublists? But spec says use for section headings, and - for any list of duties/requirements/benefits. So we can have separate sections for Responsibilities, Requirements, Skills, Benefits, Pay, Schedule. However we should only include sections that have real specific content. Pay: we have compensation details. Schedule: not mentioned (full-time but not schedule specifics). So we may omit Schedule. Benefits: we have "Why Join?" includes some benefits like work with cutting-edge AI companies, build real AI systems, contribute directly, join technical team, competitive compensation, visa support. That's more like benefits. Could also include equity etc. So we can have Benefits section.
Pay: we have compensation details: base and OTE + equity. So include Pay.
We also have Visa info; could be part of Benefits or maybe a separate note but not required.
We need to avoid boilerplate like location, employment type, experience? Those are metadata; we can fold into a short
or drop. The instruction: "Do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short
or drop it." So we can include a short paragraph for location, employment type, experience, visa maybe.
Thus final HTML: maybe start with a
containing location, employment type, experience, visa. Then
About the role
... paragraph.
Then Responsibilities
- ... each duty.
Then Requirements
- ... required items.
Then Skills
- ... technical experience list.
Then Benefits
- ... why join items.
Then Pay
... compensation details.
We must not repeat job title as heading. Not include schedule.
Check for filler: we have specific content for each.
Now extract details.
Location paragraph: "Location: New York, NY / San Mateo, CA / Remote, USA Employment Type: Full-time Experience: 3–10 years Visa: H-1B transfers and TN visas sponsored; O-1 considered case-by-case"
We can combine into a short paragraph: "Location: New York, NY / San Mateo, CA / Remote, USA. Employment Type: Full-time. Experience: 3–10 years. Visa: H-1B transfers and TN visas sponsored; O-1 considered case‑by‑case."
Now About The Role paragraph: "We’re hiring an AI Field Engineer / Forward Deployed Engineer (FDE) to join our AI’s high-velocity team You’ll work directly with ambitious AI-native companies to turn complex AI challenges into production-ready systems. This is a highly hands-on role sitting at the intersection of AI engineering, product, and customer delivery. You’ll build POCs, MVPs, and production integrations while working directly with customers, account executives, and engineering teams. You’ll also engage with technical and executive stakeholders to discuss architecture, strategy, and business outcomes. This is not a traditional consulting or solutions role you’ll be hands-on-keyboard, writing code, deploying systems, solving technical problems, and contributing directly to our products and platform."
We need to keep as prose.
Responsibilities list from "What You’ll Do": each line starts with a verb. Let's list:
- Work directly with customers to understand their technical and business challenges.
- Design and build POCs, MVPs, and production AI applications.
- Deploy and optimize LLM and generative AI workloads in production.
- Work with open-source models, inference infrastructure, and AI/ML platforms.
- Collaborate with customer engineering teams, account executives, and Fireworks product/engineering teams.
- Translate customer requirements into scalable technical solutions.
- Troubleshoot complex AI, infrastructure, and deployment challenges.
- Communicate technical architecture and tradeoffs to both engineers and executive stakeholders.
- Contribute code and product improvements directly to the codebase.
- Turn customer learnings into product feedback and help shape our roadmap.
- Take ownership of projects from initial discovery through production deployment.
Now Requirements: from "What We’re Looking For" plus "Required". The "What We’re Looking For" includes backgrounds list: Forward Deployed Engineer, AI/ML Engineer, Applied AI Engineer, AI Solutions Engineer, Solutions Architect, Sales Engineer, Customer Engineer, ML/AI Infrastructure Engineer, Customer Success Engineer with strong technical depth. That's more like preferred backgrounds. Then "Required" list:
- 3–10 years of experience in a client-facing technical or AI/ML engineering role.
- Strong Python programming skills.
- Experience building and deploying AI/ML or generative AI applications.
- Experience working with LLMs and/or open-source AI models.
- Ability to work directly with customers and communicate technical concepts clearly.
- Experience building POCs and taking solutions toward production.
- Strong problem-solving skills and the ability to operate effectively in ambiguous environments.
- Willingness to work hands-on across engineering, customer delivery, and product.
We could combine both into Requirements section. But we might separate "Qualifications" for backgrounds and "Requirements" for required. However spec says we can have sections like Requirements, Qualifications, Skills. To avoid thin sections, we could have Requirements (including both backgrounds and required) and Skills (technical experience). Let's do that.
Thus Requirements section: list of backgrounds + required items.
But we need to avoid duplication. We'll list backgrounds as bullet points, then required as bullet points.
Now Skills section: "Technical Experience Experience with some of the following is highly relevant: Python vLLM SGLang TensorRT-LLM Kubernetes AWS, Azure, or GCP AWS Bedrock / SageMaker Azure AI Foundry GCP Vertex AI LLM fine-tuning, including SFT, DPO, or RFT GPU infrastructure Model inference and optimization You do not need experience with every technology listed, but you should have strong fundamentals in AI/ML engineering and be comfortable learning quickly."
We'll list each as bullet.
Now Benefits section: from "Why Join?":
- Work directly with cutting-edge AI companies.
- Build real AI systems that move from prototype to production.
- Work across engineering, product, and customer delivery.
- Contribute directly to an AI infrastructure platform.
- Join a highly technical, builder-first team.
- Competitive compensation of $220K–$285K OTE + meaningful equity.
- US visa support including H-1B transfers and TN visas.
Note compensation already in Pay section; we might avoid duplication. But we can keep compensation in Pay only, and Benefits list other items. However the Why Join includes compensation; we could still include it in Benefits but then Pay duplicates. Better to keep Pay separate and Benefits exclude compensation line. So Benefits list: work directly with cutting-edge AI companies; build real AI systems that move from prototype to production; work across engineering, product, and customer delivery; contribute directly to an AI infrastructure platform; join a highly technical, builder-first team; US visa support including H-1B transfers and TN visas.
Now Pay section: compensation details: "$176K–$228K base | $220K–$285K OTE + meaningful equity". We'll put as paragraph.
Now ensure we don't include filler like "commensurate with experience". Not present.
Now produce HTML.
We need to output only HTML fragment, no markdown fences.
Let's craft:
Location: New York, NY / San Mateo, CA / Remote, USA. Employment Type: Full-time. Experience: 3–10 years. Visa: H-1B transfers and TN visas sponsored; O-1 considered case‑by‑case.
About the role
We’re hiring an AI Field Engineer / Forward Deployed Engineer (FDE) to join our AI’s high-velocity team You’ll work directly with ambitious AI-native companies to turn complex AI challenges into production-ready systems. This is a highly hands-on role sitting at the intersection of AI engineering, product, and customer delivery. You’ll build POCs, MVPs, and production integrations while working directly with customers, account executives, and engineering teams. You’ll also engage with technical and executive stakeholders to discuss architecture, strategy, and business outcomes. This is not a traditional consulting or solutions role you’ll be hands-on-keyboard, writing code, deploying systems, solving technical problems, and contributing directly to our products and platform.
Responsibilities
- Work directly with customers to understand their technical and business challenges.
- Design and build POCs, MVPs, and production AI applications.
- Deploy and optimize LLM and generative AI workloads in production.
- Work with open-source models, inference infrastructure, and AI/ML platforms.
- Collaborate with customer engineering teams, account executives, and Fireworks product/engineering teams.
- Translate customer requirements into scalable technical solutions.
- Troubleshoot complex AI, infrastructure, and deployment challenges.
- Communicate technical architecture and tradeoffs to both engineers and executive stakeholders.
- Contribute code and product improvements directly to the codebase.
- Turn customer learnings into product feedback and help shape our roadmap.
- Take ownership of projects from initial discovery through production deployment.
Requirements
- Backgrounds such as Forward Deployed Engineer, AI/ML Engineer, Applied AI Engineer, AI Solutions Engineer, Solutions Architect, Sales Engineer, Customer Engineer, ML/AI Infrastructure Engineer, Customer Success Engineer with strong technical depth.
- 3–10 years of experience in a client-facing technical or AI/ML engineering role.
- Strong Python programming skills.
- Experience building and deploying AI/ML or generative AI applications.
- Experience working with LLMs and/or open-source AI models.
- Ability to work directly with customers and communicate technical concepts clearly.
- Experience building POCs and taking solutions toward production.
- Strong problem-solving skills and the ability to operate effectively in ambiguous environments.
- Willingness to work hands-on across engineering, customer delivery, and product.
Skills
- Python
- vLLM
- SGLang
- TensorRT-LLM
- Kubernetes
- AWS, Azure, or GCP
- AWS Bedrock / SageMaker
- Azure AI Foundry
- for any list of duties/requirements/benefits. So we can have separate sections for Responsibilities, Requirements, Skills, Benefits, Pay, Schedule. However we should only include sections that have real specific content. Pay: we have compensation details. Schedule: not mentioned (full-time but not schedule specifics). So we may omit Schedule. Benefits: we have "Why Join?" includes some benefits like work with cutting-edge AI companies, build real AI systems, contribute directly, join technical team, competitive compensation, visa support. That's more like benefits. Could also include equity etc. So we can have Benefits section.
Pay: we have compensation details: base and OTE + equity. So include Pay.
We also have Visa info; could be part of Benefits or maybe a separate note but not required.
We need to avoid boilerplate like location, employment type, experience? Those are metadata; we can fold into a short
or drop. The instruction: "Do NOT put Job ID / requisition numbers as headings — fold minor metadata into a short
or drop it." So we can include a short paragraph for location, employment type, experience, visa maybe. Thus final HTML: maybe start with a
containing location, employment type, experience, visa. Then
About the role
... paragraph.
ThenResponsibilities
- ... each duty.
Requirements
- ... required items.
Skills
- ... technical experience list.
Benefits
- ... why join items.
Pay
... compensation details.
We must not repeat job title as heading. Not include schedule. Check for filler: we have specific content for each. Now extract details. Location paragraph: "Location: New York, NY / San Mateo, CA / Remote, USA Employment Type: Full-time Experience: 3–10 years Visa: H-1B transfers and TN visas sponsored; O-1 considered case-by-case" We can combine into a short paragraph: "Location: New York, NY / San Mateo, CA / Remote, USA. Employment Type: Full-time. Experience: 3–10 years. Visa: H-1B transfers and TN visas sponsored; O-1 considered case‑by‑case." Now About The Role paragraph: "We’re hiring an AI Field Engineer / Forward Deployed Engineer (FDE) to join our AI’s high-velocity team You’ll work directly with ambitious AI-native companies to turn complex AI challenges into production-ready systems. This is a highly hands-on role sitting at the intersection of AI engineering, product, and customer delivery. You’ll build POCs, MVPs, and production integrations while working directly with customers, account executives, and engineering teams. You’ll also engage with technical and executive stakeholders to discuss architecture, strategy, and business outcomes. This is not a traditional consulting or solutions role you’ll be hands-on-keyboard, writing code, deploying systems, solving technical problems, and contributing directly to our products and platform." We need to keep as prose. Responsibilities list from "What You’ll Do": each line starts with a verb. Let's list: - Work directly with customers to understand their technical and business challenges. - Design and build POCs, MVPs, and production AI applications. - Deploy and optimize LLM and generative AI workloads in production. - Work with open-source models, inference infrastructure, and AI/ML platforms. - Collaborate with customer engineering teams, account executives, and Fireworks product/engineering teams. - Translate customer requirements into scalable technical solutions. - Troubleshoot complex AI, infrastructure, and deployment challenges. - Communicate technical architecture and tradeoffs to both engineers and executive stakeholders. - Contribute code and product improvements directly to the codebase. - Turn customer learnings into product feedback and help shape our roadmap. - Take ownership of projects from initial discovery through production deployment. Now Requirements: from "What We’re Looking For" plus "Required". The "What We’re Looking For" includes backgrounds list: Forward Deployed Engineer, AI/ML Engineer, Applied AI Engineer, AI Solutions Engineer, Solutions Architect, Sales Engineer, Customer Engineer, ML/AI Infrastructure Engineer, Customer Success Engineer with strong technical depth. That's more like preferred backgrounds. Then "Required" list: - 3–10 years of experience in a client-facing technical or AI/ML engineering role. - Strong Python programming skills. - Experience building and deploying AI/ML or generative AI applications. - Experience working with LLMs and/or open-source AI models. - Ability to work directly with customers and communicate technical concepts clearly. - Experience building POCs and taking solutions toward production. - Strong problem-solving skills and the ability to operate effectively in ambiguous environments. - Willingness to work hands-on across engineering, customer delivery, and product. We could combine both into Requirements section. But we might separate "Qualifications" for backgrounds and "Requirements" for required. However spec says we can have sections like Requirements, Qualifications, Skills. To avoid thin sections, we could have Requirements (including both backgrounds and required) and Skills (technical experience). Let's do that. Thus Requirements section: list of backgrounds + required items. But we need to avoid duplication. We'll list backgrounds as bullet points, then required as bullet points. Now Skills section: "Technical Experience Experience with some of the following is highly relevant: Python vLLM SGLang TensorRT-LLM Kubernetes AWS, Azure, or GCP AWS Bedrock / SageMaker Azure AI Foundry GCP Vertex AI LLM fine-tuning, including SFT, DPO, or RFT GPU infrastructure Model inference and optimization You do not need experience with every technology listed, but you should have strong fundamentals in AI/ML engineering and be comfortable learning quickly." We'll list each as bullet. Now Benefits section: from "Why Join?": - Work directly with cutting-edge AI companies. - Build real AI systems that move from prototype to production. - Work across engineering, product, and customer delivery. - Contribute directly to an AI infrastructure platform. - Join a highly technical, builder-first team. - Competitive compensation of $220K–$285K OTE + meaningful equity. - US visa support including H-1B transfers and TN visas. Note compensation already in Pay section; we might avoid duplication. But we can keep compensation in Pay only, and Benefits list other items. However the Why Join includes compensation; we could still include it in Benefits but then Pay duplicates. Better to keep Pay separate and Benefits exclude compensation line. So Benefits list: work directly with cutting-edge AI companies; build real AI systems that move from prototype to production; work across engineering, product, and customer delivery; contribute directly to an AI infrastructure platform; join a highly technical, builder-first team; US visa support including H-1B transfers and TN visas. Now Pay section: compensation details: "$176K–$228K base | $220K–$285K OTE + meaningful equity". We'll put as paragraph. Now ensure we don't include filler like "commensurate with experience". Not present. Now produce HTML. We need to output only HTML fragment, no markdown fences. Let's craft:Location: New York, NY / San Mateo, CA / Remote, USA. Employment Type: Full-time. Experience: 3–10 years. Visa: H-1B transfers and TN visas sponsored; O-1 considered case‑by‑case.
About the role
We’re hiring an AI Field Engineer / Forward Deployed Engineer (FDE) to join our AI’s high-velocity team You’ll work directly with ambitious AI-native companies to turn complex AI challenges into production-ready systems. This is a highly hands-on role sitting at the intersection of AI engineering, product, and customer delivery. You’ll build POCs, MVPs, and production integrations while working directly with customers, account executives, and engineering teams. You’ll also engage with technical and executive stakeholders to discuss architecture, strategy, and business outcomes. This is not a traditional consulting or solutions role you’ll be hands-on-keyboard, writing code, deploying systems, solving technical problems, and contributing directly to our products and platform.
Responsibilities
- Work directly with customers to understand their technical and business challenges.
- Design and build POCs, MVPs, and production AI applications.
- Deploy and optimize LLM and generative AI workloads in production.
- Work with open-source models, inference infrastructure, and AI/ML platforms.
- Collaborate with customer engineering teams, account executives, and Fireworks product/engineering teams.
- Translate customer requirements into scalable technical solutions.
- Troubleshoot complex AI, infrastructure, and deployment challenges.
- Communicate technical architecture and tradeoffs to both engineers and executive stakeholders.
- Contribute code and product improvements directly to the codebase.
- Turn customer learnings into product feedback and help shape our roadmap.
- Take ownership of projects from initial discovery through production deployment.
Requirements
- Backgrounds such as Forward Deployed Engineer, AI/ML Engineer, Applied AI Engineer, AI Solutions Engineer, Solutions Architect, Sales Engineer, Customer Engineer, ML/AI Infrastructure Engineer, Customer Success Engineer with strong technical depth.
- 3–10 years of experience in a client-facing technical or AI/ML engineering role.
- Strong Python programming skills.
- Experience building and deploying AI/ML or generative AI applications.
- Experience working with LLMs and/or open-source AI models.
- Ability to work directly with customers and communicate technical concepts clearly.
- Experience building POCs and taking solutions toward production.
- Strong problem-solving skills and the ability to operate effectively in ambiguous environments.
- Willingness to work hands-on across engineering, customer delivery, and product.
Skills
- Python
- vLLM
- SGLang
- TensorRT-LLM
- Kubernetes
- AWS, Azure, or GCP
- AWS Bedrock / SageMaker
- Azure AI Foundry