Fireworks AI

1 month ago

AI Field Engineer - Enterprise

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 3 hours agoStatus: Live

Job Summary

Embedded with enterprise customers to turn complex GenAI problems into production systems. Build end-to-end POCs, MVPs, and deployments, while leading discovery conversations and executive-level alignment. Combines hands-on software delivery with customer-facing strategy, delivering scalable AI inference solutions across large organizations.

Location: New York, San Mateo

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key 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.

Pay and Benefits

Salary: USD 200,000 - 260,000 annually

Equity and Bonus:Equity

Key Requirements

•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 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 (SFT required; DPO/RFT a strong plus).
•Experience with cloud infrastructure (AWS, Azure, GCP) and deploying models on GPU infrastructure; exceptional communication skills.

Experience:5+ yearsAIGenAIInferenceEnterprise

Skills:PythonKubernetesInfrastructureCloudGPUProductionLatencyThroughputSFTDPORFTSGLangVLLMTensorRT-LLMAWSAzureGCPTensorRTGitLoad testing

Languages:English

Tech Stack:PythonKubernetesAWSAzureGCPVLLMSGLangTensorRT-LLMGPUSFTDPORFTLoad testing

Company Brief

Fireworks AI

Develops AI-driven tools to generate and optimize visual marketing content for brands and creators, automating production of short-form videos and multimedia assets for social platforms to improve engagement and scale creative workflows.

Industry: SaaS

Website

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Fireworks AI

1 month ago

AI Field Engineer - Enterprise

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 3 hours agoStatus: Live

Job Summary

Location: New York, San Mateo

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key 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.

Pay and Benefits

Salary: USD 200,000 - 260,000 annually

Equity and Bonus:Equity

Key Requirements

•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 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 (SFT required; DPO/RFT a strong plus).
•Experience with cloud infrastructure (AWS, Azure, GCP) and deploying models on GPU infrastructure; exceptional communication skills.

Experience:5+ yearsAIGenAIInferenceEnterprise

Skills:PythonKubernetesInfrastructureCloudGPUProductionLatencyThroughputSFTDPORFTSGLangVLLMTensorRT-LLMAWSAzureGCPTensorRTGitLoad testing

Languages:English

Tech Stack:PythonKubernetesAWSAzureGCPVLLMSGLangTensorRT-LLMGPUSFTDPORFTLoad testing

Company Brief

Fireworks AI

Industry: SaaS

Website