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 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-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 stackPython, 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
- 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
