AI Field Engineer at Fireworks AI embedding with customers to build production AI systems. Hands-on technical delivery of inference infrastructure, fine-tuning pipelines, and model deployments for AI-native companies.
In the last few months alone we launched Fireworks Training, partnered with Microsoft Azure Foundry, and published research straight from our production systems. A few examples of what that looks like in practice:
Frontier RL is cheaper than the mega-cluster narrative suggests: we ran cross-region rollouts using 98% sparse weight deltas and published what we learned. (blog)
Open source agents with frontier advisors: matching frontier performance through training and harness engineering. (blog)
The fine-tuning bottleneck is not the algorithm: integration friction and iteration speed are what actually stall teams; we documented the patterns across dozens of customer engagements. (blog)
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.
The Segment
As a Field Engineer in the AI Native segment you will work with the most innovative AI-native companies building at the frontier, where GenAI is the core product, not a feature, and where Fireworks is the platform they depend on to ship and scale it. These engagements move fast with fewer stakeholders, so you will spend more time in the code and iterate alongside their engineering teams, while still holding executive-level conversations on architecture and strategy. You will embed deeply with a small set of high-velocity accounts where the quality of your engineering is the relationship.
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
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. Embed with their engineering team as a peer, your credibility comes from what you build alongside them.
Spend time on-site with customers. Build trust and momentum in person, embedding with their teams where the work happens.
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.
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 is a strong signal.
Track record taking GenAI POCs from prototype to production-scale deployments.
Experience with hyperscaler AI platforms (Azure AI Foundry, AWS Bedrock/SageMaker, GCP Vertex).