# AI Field Engineer

**Company:** [Fireworks AI](https://hotfix.jobs/companies/fireworks-ai)
**Location:** San Mateo, CA, New York, NY
**Role:** Solutions Architecture
**Salary:** $200k – $260k/yr
**Experience:** 5+ years
**Skills:** Python, Kubernetes, AWS, Azure, GCP, vLLM, sglang, tensorrt-llm, llm fine-tuning, sft, dpo, rft, gpu infrastructure, Agentic Systems
**Posted:** 2026-06-09

> AI Field Engineers embed with innovative AI-native customers and partners to build POCs, MVPs, production inference deployments, fine-tuning pipelines, and evaluation frameworks. They combine hands-on engineering (Python, Kubernetes, LLM serving) with stakeholder management and on-site customer collaboration to drive GenAI products from prototype to scale while feeding insights back to the Fireworks roadmap.

## Job Description

## What You'll Work On

### Technical Delivery and Deployment
- Build end-to-end POCs and MVPs alongside customer engineering teams, working inside their codebases, infrastructure, and constraints.
- For customers whose core product is built on GenAI, architect the inference foundations that capability depends on, and size deployments so they can scale in their market without infrastructure becoming the bottleneck.
- Run load tests and establish latency, throughput, and cost baselines against realistic customer traffic profiles, and tune deployments to hit those targets.
- Deploy and validate new model families on inference frameworks (vLLM, SGLang), determining optimal shapes, quantization configs, and serving patterns across workloads.

### Model Strategy and Fine-Tuning
- Guide customers on model selection, fine-tuning strategy (SFT, DPO, RFT), and evaluation methodology.
- Build and run fine-tuning pipelines directly with customers, navigating trade-offs between model families, compute cost, and quality targets.
- Design and implement evaluation frameworks that measure production-quality metrics, not just benchmark scores.

### Customer Engagement and Stakeholder Management
- Help customers bake frontier model capabilities into their core offering and turn that into a durable competitive edge.
- Lead structured discovery conversations to unpack customer pain points, constraints, and success criteria before proposing solutions.
- Own the technical relationship from first engagement through production deployment. Embed with their engineering team as a peer.
- Spend time on-site with customers. Build trust and momentum in person.

### Product Feedback and Platform Improvement
- Identify recurring customer pain points and translate them into concrete product proposals, working directly with engineering and product to ship fixes and features.
- Codify repeatable deployment patterns and contribute them back to internal tooling, documentation, and the platform itself.
- Feed customer signals (deployment patterns, failure modes, feature gaps) back into the product roadmap with specificity and urgency.

## What We're Looking For

### Minimum Qualifications
- 5+ years in a hands-on, customer-facing technical role: Forward Deployed Engineer, Applied AI Engineer, Solutions Architect, ML Engineer with field exposure, or technical founder.
- Demonstrated ability to build production software with customers, not just advise on it. You have shipped code running in someone else's production environment.
- Strong Python skills. Comfortable reading, writing, and debugging production code.
- Familiarity with Kubernetes and infrastructure engineering.
- Working knowledge of the LLM stack: inference trade-offs, model serving, fine-tuning workflows (SFT at minimum; DPO/RFT a strong plus).
- Experience with cloud infrastructure (AWS, Azure, GCP) and deploying models on GPU infrastructure.
- Exceptional communication: able to run a sharp discovery call, present to a VP, and debug a latency issue with an ML engineer in the same afternoon.
- Experience building or integrating agentic systems, tool-use chains, or AI-native developer toolchains.

### Preferred Qualifications
- 10+ years in technical field or engineering roles.
- Experience with inference serving frameworks (vLLM, SGLang, TensorRT-LLM) and tuning deployments for real workloads.
- Prior experience at a company with a forward-deployed or embedded engineering model (Palantir, Scale AI, Anthropic, OpenAI, BCG X, McKinsey Quantum Black, AI Native startups with FDE motions).
- Prior experience as a technical founder or early engineer at an AI-native company.
- Track record taking GenAI POCs from prototype to production-scale deployments.
- Experience with hyperscaler AI platforms (Azure AI Foundry, AWS Bedrock/SageMaker, GCP Vertex).

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