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Fireworks is a Series D AI platform company (valued at $17.5B, backed by NVIDIA, Sequoia, AMD, and others) that enables enterprises to build, train, and serve specialized AI models tailored to their data and workflows. The company powers production AI across text, image, embedding, audio, and multimodal workloads using hundreds of state-of-the-art open models.
As an AI Field Engineer on the Enterprise track, you are the technical tip of the spear, embedding directly with Fireworks' most ambitious customers and technology partners to turn complex AI problems into production systems. This is a hands-on-keyboard role that sits at the intersection of engineering, product, and customer delivery.
You will spend most of your time building: shipping code, running benchmarks, debugging production issues, and architecting deployments. You build end-to-end POCs and MVPs alongside customer engineering teams, working inside their codebases and infrastructure. For customers whose core product is built on GenAI, you architect the inference foundations and size deployments to scale without infrastructure becoming a bottleneck. You run load tests, establish latency and throughput baselines against realistic traffic, and tune deployments to hit targets. You deploy and validate new model families on inference frameworks (vLLM, SGLang), determining optimal shapes, quantization configs, and serving patterns.
Beyond coding, you guide customers on model selection, fine-tuning strategy (SFT, DPO, RFT), and evaluation methodology. You build and run fine-tuning pipelines directly with customers, navigating trade-offs between model families, compute cost, and quality targets. You design evaluation frameworks that measure production-quality metrics, not just benchmark scores.
You also lead discovery conversations to unpack customer pain points and constraints, own the technical relationship from first engagement through production deployment, and spend time on-site with customers to build trust and momentum in person. You translate recurring customer pain points into concrete product proposals, working directly with engineering and product to ship fixes and features. You 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, or ML Engineer with field experience). You should be comfortable with Python, inference frameworks, and model deployment at scale. Experience with fine-tuning, quantization, and production ML systems is expected. You must be willing to travel and embed on-site with customers.
