Fireworks AI

1 day ago

AI Field Engineer, Singapore

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 19 hours agoStatus: Live

Job Summary

Embed with enterprise customers and technology partners to turn complex GenAI challenges into production systems. Build end-to-end POCs and MVPs, run benchmarks, debug issues, and architect scalable inference deployments. Advise on model selection and fine-tuning strategies, validate model families on inference frameworks (vLLM, SGLang), and translate customer pain points into product improvements—while leading discovery and executive conversations.

Location: Singapore

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Build end-to-end POCs and MVPs with customer engineering teams, working inside their codebases and constraints.
•Architect inference foundations and size deployments so customers can scale without infrastructure becoming the bottleneck.
•Run load tests and establish latency, throughput, and cost baselines; tune deployments to meet targets.
•Deploy and validate new model families on inference frameworks (vLLM, SGLang), determining shapes, quantization configs, and serving patterns.
•Lead discovery and stakeholder management from first engagement through production deployment, translating customer pain points into product improvements.

Pay and Benefits

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).
•Demonstrated ability to build production software with customers, including shipping code running in someone else’s production environment.
•Strong Python skills, with comfort reading, writing, and debugging production code; familiarity with Kubernetes and infrastructure engineering.
•Working knowledge of the LLM stack, including inference trade-offs, model serving, and fine-tuning workflows (SFT minimum; DPO/RFT a plus).
•Experience with cloud infrastructure (AWS, Azure, GCP) and deploying models on GPU infrastructure, plus exceptional communication for discovery and VP-level discussions.

Experience:5+ yearsGenAILLM inferenceEnterpriseCloudGPU infrastructure

Skills:CommunicationStakeholder managementProblem-solvingDebuggingExecutive presence

Languages:English

Tech Stack:PythonKubernetesAWSAzureGCPGPU infrastructureVLLMSGLangTensorRT-LLMSFTDPORFTLoad testingAzure AI FoundryAWS BedrockSageMakerGCP VertexPyTorchGoogle Vertex AIFunction calling

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 day ago

AI Field Engineer, Singapore

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 19 hours agoStatus: Live

Job Summary

Location: Singapore

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Build end-to-end POCs and MVPs with customer engineering teams, working inside their codebases and constraints.
•Architect inference foundations and size deployments so customers can scale without infrastructure becoming the bottleneck.
•Run load tests and establish latency, throughput, and cost baselines; tune deployments to meet targets.
•Deploy and validate new model families on inference frameworks (vLLM, SGLang), determining shapes, quantization configs, and serving patterns.
•Lead discovery and stakeholder management from first engagement through production deployment, translating customer pain points into product improvements.

Pay and Benefits

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).
•Demonstrated ability to build production software with customers, including shipping code running in someone else’s production environment.
•Strong Python skills, with comfort reading, writing, and debugging production code; familiarity with Kubernetes and infrastructure engineering.
•Working knowledge of the LLM stack, including inference trade-offs, model serving, and fine-tuning workflows (SFT minimum; DPO/RFT a plus).
•Experience with cloud infrastructure (AWS, Azure, GCP) and deploying models on GPU infrastructure, plus exceptional communication for discovery and VP-level discussions.

Experience:5+ yearsGenAILLM inferenceEnterpriseCloudGPU infrastructure

Skills:CommunicationStakeholder managementProblem-solvingDebuggingExecutive presence

Languages:English

Tech Stack:PythonKubernetesAWSAzureGCPGPU infrastructureVLLMSGLangTensorRT-LLMSFTDPORFTLoad testingAzure AI FoundryAWS BedrockSageMakerGCP VertexPyTorchGoogle Vertex AIFunction calling

Company Brief

Fireworks AI

Industry: SaaS

Website