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AI Field Engineer

Fireworks AI
Location
US
Workplace
Onsite
Employment
Full Time
Salary
USD 200,000–260,000/yr
Apply on the employer’s site

Posted 3mo ago

The employer’s full description could not be read from their board. This is a summary of the posting — follow the apply link for the original.

Responsibilities

  • Build end-to-end POCs and MVPs alongside customer engineering teams
  • Architect inference foundations for GenAI core products
  • Size deployments for market scaling
  • Run load tests and establish performance baselines
  • Tune deployments to hit performance targets
  • Deploy and validate new model families on inference frameworks
  • Determine optimal model shapes, quantization configs, and serving patterns
  • Guide customers on model selection and fine-tuning strategy
  • Build and run fine-tuning pipelines
  • Navigate trade-offs between model families, compute cost, and quality
  • Design and implement evaluation frameworks
  • Bake frontier model capabilities into customer core offerings
  • Lead structured discovery conversations
  • Unpack customer pain points, constraints, and success criteria
  • Own the technical relationship from engagement to deployment
  • Embed with customer engineering teams as a peer
  • Spend time on-site with customers to build trust and momentum
  • Identify recurring customer pain points and translate into product proposals
  • Work directly with engineering and product to ship fixes and features
  • Codify repeatable deployment patterns for internal tooling and documentation
  • Feed customer signals back into the product roadmap

Requirements

  • 5+ years in a hands-on, customer-facing technical role
  • Demonstrated ability to build production software with customers
  • 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
  • Experience with cloud infrastructure (AWS, Azure, GCP)
  • Experience deploying models on GPU infrastructure
  • Exceptional communication skills
  • Experience building or integrating agentic systems, tool-use chains, or AI-native developer toolchains
  • 10+ years in technical field or engineering roles (preferred)
  • Experience with inference serving frameworks (vLLM, SGLang, TensorRT-LLM) (preferred)
  • Prior experience at a company with a forward-deployed or embedded engineering model (preferred)
  • Prior experience as a technical founder or early engineer at an AI-native company (strong signal)
  • Track record taking GenAI POCs from prototype to production-scale deployments (preferred)
  • Experience with hyperscaler AI platforms (Azure AI Foundry, AWS Bedrock/SageMaker, GCP Vertex) (preferred)

Preferred

  • 10+ years in technical field or engineering roles
  • Experience with inference serving frameworks (vLLM, SGLang, TensorRT-LLM)
  • Prior experience at a company with a forward-deployed or embedded engineering model (Palantir, Scale AI, Anthropic, OpenAI, BCG X, McKinsey Quantum Black, AI Native startups with FDE motions)
  • Prior experience as a technical founder or early engineer at an AI-native company
  • Track record taking GenAI POCs from prototype to production-scale deployments
  • Experience with hyperscaler AI platforms (Azure AI Foundry, AWS Bedrock/SageMaker, GCP Vertex)

Skills

  • PyTorch
  • AMD
  • NVIDIA
  • Python
  • Kubernetes
  • AWS
  • Azure
  • GCP
  • vLLM
  • SGLang
  • TensorRT-LLM
  • GenAI
  • LLM
  • SFT
  • DPO
  • RFT

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