Staff Forward Deployed Engineer, AI/ML

digitalocean98; United States; Hybrid; USD 220,000 - 239,000; Full time

Benefits offered by this job

Bonus

Equity

Hybrid work model

Job summary

digitalocean98 seeks a Staff Forward Deployed Engineer to act as the technical lead for AI-native customers and platform initiatives. You will bridge product engineering, AI infrastructure, and customer implementations to help strategic clients deploy and scale production AI and agentic systems.

This hybrid role emphasizes architectural design, benchmarking, and collaboration with GPU vendors and model providers.

Qualifications

• Experience in Forward Deployed Engineering or AI platform engineering for production AI systems.
• Strong knowledge of workflow orchestration, deployment systems, and scalable AI architectures.
• Production coding in Python or Go; building tooling, automation, and benchmarking.

Responsibilities

• Architect and deploy production AI and agentic systems for strategic clients.
• Benchmark and optimize AI infrastructure for latency, GPUs, and cost.
• Surface insights to product and research teams as first customer for new capabilities.
• Create scalable deployment assets and reference architectures.
• Collaborate with GPUs, model providers, and ISVs on co-development and launch readiness.
• Travel up to 30% for client engagements and events.

Skills

Forward Deployed

AI Infrastructure

Technical Consulting

Python

Go

Benchmarking

Tools

Automation Tools

Job description

Role overview

A hybrid Staff Forward Deployed Engineer role serving as the technical lead for AI-native customers and platform initiatives. The position operates at the intersection of product engineering, AI infrastructure, and customer implementation, helping strategic clients deploy and scale production AI and agentic systems, and validating new platform capabilities as the first customer.

Responsibilities

• Partner with strategic AI-native enterprises and startups to architect, deploy, and scale production AI and agentic systems
• Optimize distributed inference and runtime performance through benchmarking, GPU efficiency tuning, KV-cache optimization, speculative decoding, and latency and cost optimization
• Act as the first customer for new AI-native platform capabilities, surfacing operational insights to product engineering and research teams
• Build scalable deployment assets including benchmarking systems, automation tooling, AI starter kits, and reference architectures
• Collaborate with GPU vendors, model providers, infrastructure partners, and ISVs on co-development, validation, and launch readiness
• Travel up to 30% for customer engagements, workshops, ecosystem partnerships, and conferences

Requirements

• Experience in Forward Deployed Engineering, AI Infrastructure, Technical Consulting, or AI Platform Engineering supporting production AI systems
• Strong understanding of workflow orchestration, deployment systems, memory patterns, and AI-native application architectures
• Production coding skills in Python or Go with experience building tooling, automation systems, and benchmarking frameworks
• Proven ability to benchmark and optimize AI infrastructure with focus on scalability, reliability, GPU efficiency, and latency
• Consultative execution skills with the ability to establish technical credibility with CTOs and Principal architects
• Active contributor to open-source AI, infrastructure, orchestration, or developer tooling ecosystems

Nice to have

• Experience collaborating with GPU vendors, infrastructure providers, or model vendors on benchmarking and launch readiness initiatives

Benefits and work setup

• Hybrid role
• Compensation range of $220,000 - $239,000
• Eligibility for bonus and equity compensation