1 week 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: 7 hours agoStatus: Live

Job Summary

Build and deploy end-to-end AI POCs, MVPs, and production integrations with enterprise customers and technology partners. Architect inference foundations, run load tests, and tune latency/throughput/cost using frameworks like vLLM and SGLang. Guide model selection and fine-tuning strategies (SFT/DPO/RFT), design evaluation for production-quality metrics, and translate field learnings into product improvements while engaging executives and engineering teams.

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 inside their codebases, infrastructure, and constraints.
•Architect inference foundations and size deployments so customers can scale without infrastructure bottlenecks.
•Run load tests and establish and tune latency, throughput, and cost baselines against realistic traffic profiles.
•Deploy and validate model families on inference frameworks (vLLM, SGLang), selecting optimal shapes, quantization configs, and serving patterns.
•Lead discovery conversations, manage technical relationships through production deployment, and translate recurring customer pain points into product proposals and platform improvements.

Pay and Benefits

Salary: SGD 200,000 - 350,000 annually

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 and ship code running in customer production environments.
•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 with executives and technical teams.

Experience:5+ yearsGenAIEnterprise AIAI infrastructure

Skills:CommunicationStakeholder managementProblem-solvingDebuggingExecutive presence

Tech Stack:PyTorchAMDMicrosoft Azure FoundryAWS BedrockSageMakerGCP VertexAWSAzureGCPKubernetesVLLMSGLangTensorRT-LLMGPULLMInference

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 week 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: 7 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 inside their codebases, infrastructure, and constraints.
•Architect inference foundations and size deployments so customers can scale without infrastructure bottlenecks.
•Run load tests and establish and tune latency, throughput, and cost baselines against realistic traffic profiles.
•Deploy and validate model families on inference frameworks (vLLM, SGLang), selecting optimal shapes, quantization configs, and serving patterns.
•Lead discovery conversations, manage technical relationships through production deployment, and translate recurring customer pain points into product proposals and platform improvements.

Pay and Benefits

Salary: SGD 200,000 - 350,000 annually

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 and ship code running in customer production environments.
•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 with executives and technical teams.

Experience:5+ yearsGenAIEnterprise AIAI infrastructure

Skills:CommunicationStakeholder managementProblem-solvingDebuggingExecutive presence

Tech Stack:PyTorchAMDMicrosoft Azure FoundryAWS BedrockSageMakerGCP VertexAWSAzureGCPKubernetesVLLMSGLangTensorRT-LLMGPULLMInference

Company Brief

Fireworks AI

Industry: SaaS

Website