AI Field Engineer - AI Natives
New York, NY, Hybrid (Remote, USA), San Mateo, CA$200k - $260kFull-timePosted 4mo ago
About the role
AI Field Engineers embed with customers to turn complex AI problems into production systems, combining hands-on development with executive-level conversations on architecture and business outcomes. Role centers on technical delivery, model strategy, and customer engagement in fast-moving GenAI-focused accounts.
Responsibilities
- Build end-to-end POCs and MVPs alongside customer engineering teams, working inside their codebases, infrastructure, and constraints.
- Architect inference foundations and size deployments for GenAI-based core products to scale without infrastructure bottlenecks.
- Run load tests, establish latency/throughput/cost baselines, and tune deployments to targets.
- Deploy and validate new model families on inference frameworks (vLLM, SGLang), determining optimal shapes, quantization configs, and serving patterns.
- Guide customers on model selection, fine-tuning strategy (SFT, DPO, RFT), and evaluation methodology.
- Build and run fine-tuning pipelines with customers, balancing model families, compute cost, and quality targets.
- Design and implement evaluation frameworks measuring production-quality metrics.
- Lead structured discovery conversations, uncover pain points, constraints, and success criteria.
- Own technical relationships from first engagement through production deployment, embedding with customer teams.
- Spend time on-site with customers to build trust and momentum in person.
- Identify recurring customer pain points and translate into concrete product proposals; contribute to internal tooling and platform improvements.
- Feedback deployment patterns, failure modes, and feature gaps to product roadmap with urgency.
Qualifications
- 5+ years in a hands-on, customer-facing technical role (e.g., Forward Deployed Engineer, Applied AI Engineer, Solutions Architect, ML Engineer with field exposure, or technical founder).
- Proven track record building production software deployed in customers' environments.
- 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 including SFT; DPO/RFT a plus).
- Experience with cloud infrastructure (AWS, Azure, GCP) and deploying models on GPU infrastructure.
- Exceptional communication for discovery calls, executive presentations, and debugging latency with ML engineers.
- Experience building or integrating agentic systems, tool-use chains, or AI-native developer toolchains.
Skills
Required:PythonKubernetesLLM stackCloud platforms
Benefits
- Total compensation includes meaningful equity, competitive salary, and comprehensive benefits package.
Last updated Jul 29, 2026. Interested? Apply on the company site.
About Fireworks AI
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