AI Field Engineer - AI Natives

Fireworks • San Mateo, CA

65d • Onsite

About The Position

Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows, and products. Founded by the team behind PyTorch and backed by AMD, Atreides, Benchmark Capital, Index Ventures, Lightspeed, NVIDIA, Sequoia Capital, and TCV, Fireworks powers production AI with hundreds of state-of-the-art open models across text, image, embedding, audio, and multimodal workloads. Today, Fireworks is a Series D company valued at $17.5 billion, bringing together an ambitious, collaborative team that's building the future of enterprise AI.
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)

Requirements

• 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.

Nice To Haves

• 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).

Responsibilities

• Build end-to-end POCs and MVPs alongside customer engineering teams, working inside their codebases, infrastructure, and constraints.
• 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.
• 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, 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.
• 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.

Benefits

• Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving.
• Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally.
• Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results.
• Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed