Description review
AI Field Engineer – Enterprise (Remote U.S.)
Morgan Pinnacle Group · United States · back to the listing
HR standards
65/100
needs work
Title ↔ description
73/100
solid
Reads as
Machine Learning Engineer
84% confident
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
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What the listing never says
- 56 bullet points. Long requirement lists deter qualified candidates, who read them as hard gates. Scope clarity
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
AI Field Engineer – Enterprise
Full-Time | Remote (U.S.)
Compensation: $176,000 – $224,000 Base
On-Target Earnings (OTE): $220,000 – $280,000 + Meaningful Equity
About the Role
We are looking for an AI Field Engineer (Enterprise) with 3+ years of experience to embed with ambitious enterprise customers and turn complex GenAI challenges into production systems—fast.
You'll combine deep hands-on engineering with the executive presence to earn trust across large organizations and help drive engagements from initial technical discovery through production deployment.
What You'll Do
• Lead technical discovery calls, scope proof-of-concepts, and run load tests and evaluations to validate the right model architecture and deployment configuration for enterprise customers.
• Build end-to-end POCs and production integrations directly inside customer environments while navigating infrastructure, security requirements, and organizational constraints.
• Guide customers on model selection, fine-tuning strategies (SFT, DPO, RFT), and evaluation frameworks to move from experimentation to production at scale.
• Manage relationships across multiple enterprise stakeholders, identifying technical champions and helping align teams to move projects forward efficiently.
• Provide recurring customer feedback and deployment insights to the engineering organization to help shape future product development.
Required Qualifications
• Deep hands-on experience with LLM inference and/or training.
• Working knowledge of open-model frameworks such as:
• vLLM
• SGLang
• TensorRT-LLM
• Experience with fine-tuning workflows:
• SFT required
• DPO and/or RFT strongly preferred
• Candidates whose experience is limited to closed-model API integrations alone will not be a fit.
• Proven ability to build and deploy production code within customer environments, including POCs or MVPs running in production.
• Strong Python programming skills.
• Experience with GPU infrastructure.
• Experience using AWS, Azure, or GCP.
• Experience with Kubernetes.
• Strong communication skills with the ability to engage both technical and executive audiences.
• Customer-facing engineering experience in roles such as:
• Field Engineer
• Applied AI Engineer
• Solutions Architect
• AI Infrastructure Engineer
• ML Engineer
• Software Engineer with pre-sales exposure
• 3+ years of relevant experience.
Preferred Qualifications
• Experience with DPO or reinforcement fine-tuning (RFT).
• Experience deploying enterprise-scale GenAI applications.
• Experience working directly with production AI infrastructure.
• Interest in contributing to product direction based on customer feedback.
• Comfortable working in fast-paced, high-growth environments with significant ownership.
Technical Environment
• Python
• vLLM
• SGLang
• TensorRT-LLM
• Kubernetes
• AWS
• Azure
• GCP
• Azure AI Foundry
• AWS Bedrock
• AWS SageMaker
• GCP Vertex AI
• GPU Infrastructure
• Open-source LLM Frameworks
• LLM Fine-Tuning (SFT, DPO, RFT)
Compensation & Benefits
• Base Salary: $176,000 – $224,000
• On-Target Earnings: $220,000 – $280,000 (80/20 base/variable split)
• Quarterly variable compensation based on individual and team performance
• Compensation may exceed the posted range for candidates with extensive experience.
• Meaningful equity package.
Visa Sponsorship
Visa sponsorship is available.
• H-1B transfers
• TN visas
• O-1 visas considered on a case-by-case basis
Location
Remote (United States)
Employees located near company hubs may follow a hybrid schedule. Regular travel to enterprise customer sites is expected.
About the Company
This fast-growing AI infrastructure company helps organizations build, fine-tune, and scale production AI applications using open-source models. Its platform is designed to deliver high-performance inference, lower latency, and greater scalability for enterprise AI workloads.
Founded in 2021, the company has grown to approximately 211 employees and has raised over $327 million in funding. The engineering culture emphasizes technical excellence, ownership, rapid execution, and close collaboration between customer-facing teams and product engineering.
The organization values engineers who enjoy solving complex technical challenges, working directly with customers, and helping shape the future of AI infrastructure.
Originally posted on Himalayas
Full-Time | Remote (U.S.)
Compensation: $176,000 – $224,000 Base
On-Target Earnings (OTE): $220,000 – $280,000 + Meaningful Equity
About the Role
We are looking for an AI Field Engineer (Enterprise) with 3+ years of experience to embed with ambitious enterprise customers and turn complex GenAI challenges into production systems—fast.
You'll combine deep hands-on engineering with the executive presence to earn trust across large organizations and help drive engagements from initial technical discovery through production deployment.
What You'll Do
• Lead technical discovery calls, scope proof-of-concepts, and run load tests and evaluations to validate the right model architecture and deployment configuration for enterprise customers.
• Build end-to-end POCs and production integrations directly inside customer environments while navigating infrastructure, security requirements, and organizational constraints.
• Guide customers on model selection, fine-tuning strategies (SFT, DPO, RFT), and evaluation frameworks to move from experimentation to production at scale.
• Manage relationships across multiple enterprise stakeholders, identifying technical champions and helping align teams to move projects forward efficiently.
• Provide recurring customer feedback and deployment insights to the engineering organization to help shape future product development.
Required Qualifications
• Deep hands-on experience with LLM inference and/or training.
• Working knowledge of open-model frameworks such as:
• vLLM
• SGLang
• TensorRT-LLM
• Experience with fine-tuning workflows:
• SFT required
• DPO and/or RFT strongly preferred
• Candidates whose experience is limited to closed-model API integrations alone will not be a fit.
• Proven ability to build and deploy production code within customer environments, including POCs or MVPs running in production.
• Strong Python programming skills.
• Experience with GPU infrastructure.
• Experience using AWS, Azure, or GCP.
• Experience with Kubernetes.
• Strong communication skills with the ability to engage both technical and executive audiences.
• Customer-facing engineering experience in roles such as:
• Field Engineer
• Applied AI Engineer
• Solutions Architect
• AI Infrastructure Engineer
• ML Engineer
• Software Engineer with pre-sales exposure
• 3+ years of relevant experience.
Preferred Qualifications
• Experience with DPO or reinforcement fine-tuning (RFT).
• Experience deploying enterprise-scale GenAI applications.
• Experience working directly with production AI infrastructure.
• Interest in contributing to product direction based on customer feedback.
• Comfortable working in fast-paced, high-growth environments with significant ownership.
Technical Environment
• Python
• vLLM
• SGLang
• TensorRT-LLM
• Kubernetes
• AWS
• Azure
• GCP
• Azure AI Foundry
• AWS Bedrock
• AWS SageMaker
• GCP Vertex AI
• GPU Infrastructure
• Open-source LLM Frameworks
• LLM Fine-Tuning (SFT, DPO, RFT)
Compensation & Benefits
• Base Salary: $176,000 – $224,000
• On-Target Earnings: $220,000 – $280,000 (80/20 base/variable split)
• Quarterly variable compensation based on individual and team performance
• Compensation may exceed the posted range for candidates with extensive experience.
• Meaningful equity package.
Visa Sponsorship
Visa sponsorship is available.
• H-1B transfers
• TN visas
• O-1 visas considered on a case-by-case basis
Location
Remote (United States)
Employees located near company hubs may follow a hybrid schedule. Regular travel to enterprise customer sites is expected.
About the Company
This fast-growing AI infrastructure company helps organizations build, fine-tune, and scale production AI applications using open-source models. Its platform is designed to deliver high-performance inference, lower latency, and greater scalability for enterprise AI workloads.
Founded in 2021, the company has grown to approximately 211 employees and has raised over $327 million in funding. The engineering culture emphasizes technical excellence, ownership, rapid execution, and close collaboration between customer-facing teams and product engineering.
The organization values engineers who enjoy solving complex technical challenges, working directly with customers, and helping shape the future of AI infrastructure.
Originally posted on Himalayas