AI Field Engineer

Role Description

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.
• You lead discovery conversations, align stakeholders, and translate customer pain points into product improvements.
• This is a role for engineers who are comfortable on-site with customers, building relationships and trust in person.

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.
• 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 skills.
• Experience building or integrating agentic systems, tool-use chains, or AI-native developer toolchains.

Requirements

• Build end-to-end POCs and MVPs alongside customer engineering teams.
• Architect the inference foundations that capability depends on for customers whose core product is built on GenAI.
• Run load tests and establish latency, throughput, and cost baselines against realistic customer traffic profiles.
• Deploy and validate new model families on inference frameworks (vLLM, SGLang).
• Guide customers on model selection, fine-tuning strategy (SFT, DPO, RFT), and evaluation methodology.
• Build and run fine-tuning pipelines directly with customers.
• Design and implement evaluation frameworks that measure production-quality metrics.
• Help customers bake frontier model capabilities into their core offering.
• Lead structured discovery conversations to unpack customer pain points, constraints, and success criteria.
• Own the technical relationship from first engagement through production deployment.
• Spend time on-site with customers to build trust and momentum.
• Identify recurring customer pain points and translate them into concrete product proposals.
• Codify repeatable deployment patterns and contribute them back to internal tooling.
• Feed customer signals back into the product roadmap with specificity and urgency.

Benefits

• Total compensation includes meaningful equity in a fast-growing startup.
• Competitive salary and comprehensive benefits package.
• Base salary is determined by individual qualifications, experience, skills, interview performance, market data, and work location.
• On Target Earnings (Plus Equity): $200,000 — $260,000 USD.