AI Field Engineer - Enterprise
Fireworks AIabout 2 months ago
Base Salary
$200k - $260k/yr
Responsibilities
- Build end-to-end POCs and MVPs with customer engineering teams inside their codebases and infrastructure.
- Architect inference foundations, size deployments, and prevent infrastructure from becoming a bottleneck for GenAI products.
- Run load tests, establish latency, throughput, and cost baselines, and tune deployments to meet customer targets.
- Deploy and validate model families using inference frameworks while optimizing model shapes, quantization configurations, and serving patterns.
- Advise customers on model selection, fine-tuning strategy, and evaluation methodology, and build fine-tuning pipelines with them.
- Design and implement evaluation frameworks based on production-quality metrics.
- Lead discovery conversations, own technical relationships through production deployment, and manage relationships with engineers and executives.
- Spend time on-site with customers to build trust, momentum, and technical alignment.
- Translate recurring customer pain points into product proposals, tooling, documentation, platform improvements, and roadmap feedback.
Requirements
- At least five years of hands-on experience in a customer-facing technical role such as Forward Deployed Engineer, Applied AI Engineer, Solutions Architect, ML Engineer with field exposure, or technical founder.
- Demonstrated ability to build and ship production software inside customer environments, not just provide technical advice.
- Strong Python skills and comfort reading, writing, and debugging production code.
- Familiarity with Kubernetes and infrastructure engineering.
- Working knowledge of LLM inference trade-offs, model serving, and fine-tuning workflows, with SFT required at minimum and DPO/RFT advantageous.
- Experience with AWS, Azure, or GCP cloud infrastructure and deploying models on GPU infrastructure.
- Exceptional communication skills across discovery calls, VP-level presentations, and technical debugging with ML engineers.
- Preferred: ten or more years in technical field or engineering roles.
- Preferred: experience with vLLM, SGLang, TensorRT-LLM, and tuning inference deployments for real workloads.
- Preferred: experience operating as a technical authority within customer infrastructure and shipping production code there.
- Preferred: track record taking GenAI POCs to production-scale deployments.
- Preferred: experience with Azure AI Foundry, AWS Bedrock, AWS SageMaker, or GCP Vertex.
- Preferred: experience building or integrating agentic systems, tool-use chains, or AI-native developer toolchains.
Benefits
- Work on frontier AI infrastructure and scalable model serving problems.
- Collaborate with world-class engineers and AI researchers.
- High ownership and direct impact in a fast-growing team with minimal bureaucracy.
- Equal-opportunity employer committed to diversity and inclusion.
- The role includes on-site customer engagement and embedding with customer teams.