Note: The job is a remote job and is open to candidates in USA. Fireworks AI is building the future of generative AI infrastructure, delivering high-quality models with fast and scalable inference. The AI Field Engineer will work closely with customers to develop production systems, manage technical relationships, and drive product improvements through customer feedback.
Responsibilities
- 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
- 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. 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
Skills
- 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
- 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 with hyperscaler AI platforms (Azure AI Foundry, AWS Bedrock/SageMaker, GCP Vertex)
- Experience building or integrating agentic systems, tool-use chains, or AI-native developer toolchains
Company Overview
Company H1B Sponsorship