Fireworks AI - AI Field Engineer - Enterprise

OpenKrill; United States; Remote; USD 176,000 - 224,000; Full time

Job summary

Fireworks AI is seeking an AI Field Engineer (Enterprise) with 3+ years of experience to embed with enterprise customers and turn GenAI challenges into production systems.

This role combines hands-on engineering with executive presence to drive deals from discovery to deployment, including POCs, deployments, and customer success across diverse enterprise environments.

Qualifications

• Deep hands-on experience with LLM inference and/or training.
• Proven ability to ship production code inside a customer's environment.
• Strong Python plus GPU/cloud infrastructure experience.
• Executive presence and enterprise navigation skills.
• Pre-sales or customer-facing field engineering experience.
• 3+ years in client-facing AI/ML roles.

Responsibilities

• Lead technical discovery calls, scope POCs, and run load tests to validate model architecture and deployment configuration.
• Build end-to-end POCs and production integrations inside customer environments.
• Guide customers on model selection, fine-tuning strategies, and evaluation frameworks.
• Manage multi-stakeholder enterprise relationships and align the right people to move deals forward.
• Feed customer pain points back into the product roadmap.

Skills

LLM inference

Model fine-tuning

Python

GPU/cloud infra

Executive presence

Pre-sales / FDE

Client-facing

Enterprise navigation

Tools

vLLM

TensorRT-LLM

Kubernetes

AWS

Azure

GCP

LLM frameworks

Job description

Source: Paraform - https://www.paraform.com/share/fireworks-ai/cmqh2zxn100020bkp395s3lwjCompensation: $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 roleAI 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-­e‑d‑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

TeamField 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