Fireworks AI - AI Field Engineer - Enterprise

deCircle; New York (NY); Hybrid; USD 176,000 - 224,000; Full time

Job summary

Fireworks AI is seeking an AI Field Engineer (Enterprise) with 3+ years to embed with our enterprise customers and turn GenAI challenges into production systems. You will be the technical lead from discovery to deployment, pairing hands-on engineering with executive presence to move deals forward.

The role requires leading discovery calls, scoping POCs, building end-to-end integrations in customer environments, guiding model selection and fine-tuning, and managing multi-stakeholder relationships

Qualifications

• Deep hands-on experience with LLM inference and training.
• Knowledge of open-model frameworks (vLLM, SGLang, TensorRT-LLM).
• Experience with fine-tuning (SFT minimum; DPO/RFT a plus).
• Proven ability to ship production code inside a customer's environment.

Responsibilities

• Lead technical discovery calls and scope POCs.
• Build end-to-end POCs and production integrations inside customer environments.
• Guide customers on model selection and fine-tuning strategies.
• Manage multi-stakeholder enterprise relationships to move deals forward.

Skills

LLM inference

Open-model frameworks

Executive presence

Customer-facing

Python

Tools

Kubernetes

AWS

Azure

GCP

Vertex AI

vLLM

SGLang

TensorRT-LLM

Python

Job description

Source: Paraform - https://www.paraform.com/share/fireworks-ai/cmqh2zxn100020bkp395s3lwj

Compensation: $176,000-$224,000 base; OTE $220,000-$280,000 (80/20 base/variable, variable paid quarterly). Candidates with 10+ years may be considered above range. Meaningful equity on top of OTE.

Location: US-based, remote-friendly; offices in San Mateo, CA and New York, NY; regular on-site travel to enterprise customers. Visa: H-1B transfers and TN sponsored; O-1 case-by-case.

About this role

AI Field Engineer (Enterprise) with 3+ years of experience to embed with Fireworks AI's most ambitious enterprise customers and turn complex GenAI challenges into production systems, fast. The technical tip of the spear, pairing deep hands-on engineering with executive presence to drive deals from first discovery call to production deployment.

What will you be doing?

• Lead technical discovery calls, scope POCs, and run load tests and evaluations to validate the right model architecture and deployment configuration for each enterprise customer


• Build end-to-end POCs and production integrations hands-on-keyboard inside customer environments, navigating their infrastructure, security requirements, and organizational constraints


• Guide customers on model selection, fine-tuning strategy (SFT, DPO, RFT), and evaluation frameworks, moving them from open-model exploration to production at scale


• Manage multi-stakeholder enterprise relationships: identify technical champions, navigate org politics, and align the right people to move deals forward quickly


• Feed recurring customer pain points and deployment patterns back into the product roadmap

Team

Field engineering team led by Roberto Barroso Luque (ex-BCG X); flat, low-ego, extreme ownership; no seniority title distinctions; arrogance screened out; client-facing by week 1-2; engineers push PRs to the Fireworks codebase.

Tech stack

Python, vLLM, SGLang, TensorRT-LLM, Kubernetes, AWS, Azure, GCP, Azure AI Foundry, AWS Bedrock, AWS SageMaker, GCP Vertex AI, LLM Fine-Tuning (SFT, DPO, RFT), GPU Infrastructure

About Fireworks AI

Fastest way to build, tune and scale AI on open models; high-performance GenAI inference platform powering Uber, DoorDash, Notion, Cursor. Founded by Meta PyTorch and Google Vertex AI veterans. 211 employees, founded 2021, $1.5B total funding.

• Deep hands-on experience with LLM inference and/or training; working knowledge of open-model frameworks (vLLM, SGLang, TensorRT-LLM) and fine-tuning (SFT minimum; DPO/RFT a strong plus). Closed-model/API-wrapper-only experience will not clear the bar


• Proven ability to ship production code inside a customer's environment (built and deployed POCs/MVPs in someone else's prod), not just advisory work


• Strong Python plus GPU/cloud infrastructure (AWS, Azure, or GCP) and comfort with Kubernetes


• Executive presence and enterprise navigation: deep-dive with an ML engineer and present trade-offs to a VP the same day


• Pre-sales or customer-facing field engineering experience (FDE, Applied AI Engineer, Solutions Architect, or similar); pure SWEs without customer-facing exposure are not a fit


• 3+ years in client-facing AI/ML roles