The ad says“Experience building or integrating agentic systems, tool-use chains, or AI-native developer toolchains”
The ad says“Experience building or integrating agentic systems, tool-use chains, or AI-native developer toolchains”
The ad says“Experience with cloud infrastructure (AWS, Azure, GCP) and deploying models on GPU infrastructure”
The ad says“Experience with hyperscaler AI platforms (Azure AI Foundry, AWS Bedrock/SageMaker, GCP Vertex)”
The ad says“Experience with hyperscaler AI platforms (Azure AI Foundry, AWS Bedrock/SageMaker, GCP Vertex)”
The ad says“Experience with cloud infrastructure (AWS, Azure, GCP) and deploying models on GPU infrastructure”
The ad says“Experience with hyperscaler AI platforms (Azure AI Foundry, AWS Bedrock/SageMaker, GCP Vertex)”
The ad says“Working knowledge of the LLM stack: inference trade-offs, model serving, fine-tuning workflows (SFT at minimum; DPO/RFT a strong plus)”
The ad says“Working knowledge of the LLM stack: inference trade-offs, model serving, fine-tuning workflows (SFT at minimum; DPO/RFT a strong plus)”
The ad says“Experience with cloud infrastructure (AWS, Azure, GCP) and deploying models on GPU infrastructure”
The ad says“Experience with hyperscaler AI platforms (Azure AI Foundry, AWS Bedrock/SageMaker, GCP Vertex)”
The ad says“Experience with cloud infrastructure (AWS, Azure, GCP) and deploying models on GPU infrastructure”
The ad says“Strong Python skills. Comfortable reading, writing, and debugging production code. Familiarity with Kubernetes and infrastructure engineering”
The ad says“Strong Python skills. Comfortable reading, writing, and debugging production code. Familiarity with Kubernetes and infrastructure engineering”
The ad says“Working knowledge of the LLM stack: inference trade-offs, model serving, fine-tuning workflows (SFT at minimum; DPO/RFT a strong plus)”
The ad says“Working knowledge of the LLM stack: inference trade-offs, model serving, fine-tuning workflows (SFT at minimum; DPO/RFT a strong plus)”
The ad says“Strong Python skills. Comfortable reading, writing, and debugging production code. Familiarity with Kubernetes and infrastructure engineering”
The ad says“Working knowledge of the LLM stack: inference trade-offs, model serving, fine-tuning workflows (SFT at minimum; DPO/RFT a strong plus)”
The ad says“Working knowledge of the LLM stack: inference trade-offs, model serving, fine-tuning workflows (SFT at minimum; DPO/RFT a strong plus)”
The ad says“Experience with inference serving frameworks (vLLM, SGLang, TensorRT-LLM) and tuning deployments for real workloads”
The ad says“Experience with inference serving frameworks (vLLM, SGLang, TensorRT-LLM) and tuning deployments for real workloads”
The ad says“Experience building or integrating agentic systems, tool-use chains, or AI-native developer toolchains”
The ad says“Experience with inference serving frameworks (vLLM, SGLang, TensorRT-LLM) and tuning deployments for real workloads”
AI Field Engineers at Fireworks are the technical tip of the spear
You embed with our most ambitious customers and technology partners to turn complex AI problems into production systems, fast
The role sits at the intersection of engineering, product, and customer delivery
You are hands-on-keyboard building POCs, MVPs, and production integrations, while also holding your own in executive-level conversations about architecture, strategy, and business outcomes
You spend most of your time building
You ship code, run benchmarks, debug production issues, and architect deployments
But you also lead discovery conversations, align stakeholders, and translate customer pain points into product improvements that compress the feedback loop from field to roadmap
This is a role for engineers who are comfortable on-site with customers, building the relationships and trust that happen in person, not just over a call
As a Field Engineer in the Enterprise track you will work with large organizations and digital-native companies adopting GenAI across the business
These engagements span more stakeholders and longer cycles, so you will manage executive relationships and align teams while staying hands-on in the code
The emphasis is on pairing strong technical delivery with the executive presence to earn trust across an org: discovery, solution design, POC execution, and the path to production at enterprise scale
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
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
Many of our customers exist because of GenAI. Help them 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. Earn trust with ML engineers and VPs in the same meeting
Spend time on-site with customers. Build trust and momentum in person, embedding with their teams where the work happens
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
Preferred experience
Strong Python skills. Comfortable reading, writing, and debugging production code. Familiarity with Kubernetes and infrastructure engineering
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
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 with cloud infrastructure (AWS, Azure, GCP) and deploying models on GPU infrastructure
Working knowledge of the LLM stack: inference trade-offs, model serving, fine-tuning workflows (SFT at minimum; DPO/RFT a strong plus)
10+ years in technical field or engineering roles
Experience with inference serving frameworks (vLLM, SGLang, TensorRT-LLM) and tuning deployments for real workloads
Experience operating as a technical authority inside a customer’s environment building within their infrastructure, navigating their constraints, and shipping code that runs in their production systems
Track record taking GenAI POCs from prototype to production-scale deployments
Experience building or integrating agentic systems, tool-use chains, or AI-native developer toolchains
Experience with hyperscaler AI platforms (Azure AI Foundry, AWS Bedrock/SageMaker, GCP Vertex)
