Senior Forward Deployed Engineer II – AI/ML

DigitalOcean

. Partner with strategic AI-native enterprises and AI startups to architect, deploy, optimize, and scale production AI and agentic systems on DigitalOcean’s AI-Native Cloud .

Posted 10/15/2026full-timeRemote • Massachusetts • United StatesSenior💰 $167,000 - $209,000 per yearWebsite

Tech Stack

Tools & technologies

CloudDistributed SystemsKubernetesNode.jsPythonRayGo

About the role

Key responsibilities & impact

Partner with strategic AI-native enterprises and AI startups to architect, deploy, optimize, and scale production AI and agentic systems on DigitalOcean’s AI-Native Cloud
Support complex migrations, production-ready proofs of concept, deployment acceleration, and long-term workload expansion across inference and runtime platforms
Optimize distributed inference and runtime performance through benchmarking, GPU efficiency tuning, KV-cache optimization, speculative decoding, prefill/decode disaggregation, multi-node deployments, and latency/cost optimization
Act as the first customer for DigitalOcean’s AI-native platform capabilities, including Inference Engine, runtimes, orchestration systems, GPU platforms, and deployment workflows
Surface operational insights, architectural gaps, and scaling bottlenecks to Product Engineering and Research teams
Build benchmarking systems, automation tooling, AI starter kits, deployment frameworks, operational playbooks, finetuning workflows, and reference architectures
Collaborate with GPU vendors, model providers, infrastructure partners, and ISVs on co-development, technical validation, optimization, and launch readiness
Enable customer-facing technical teams and partner teams through deployment patterns, benchmarking insights, operational playbooks, reference architectures, demos, and technical guidance
Travel up to 30% for customer engagements, strategic onsite workshops, ecosystem partnerships, conferences, and internal collaboration
Drive production adoption of AI-native systems and help shape DigitalOcean’s AI-Native Cloud for inference and agentic workloads

Requirements

What you’ll need

Experience designing and operationalizing production AI systems, including inference workloads, agentic runtimes, orchestration frameworks, and AI-native applications
Strong hands-on experience with inference and serving frameworks such as vLLM, SGLang, Ray Serve, NVIDIA Dynamo, llm-d, or equivalent systems
Experience with LLM optimization techniques including continuous batching, quantization, KV-cache optimization, and speculative decoding
Deep expertise with NVIDIA and AMD GPU platforms and software ecosystems including CUDA, ROCm, TensorRT, Triton, NCCL, RCCL, NVLink, XGMI, and RoCE
Strong proficiency with Kubernetes, distributed systems, networking, storage systems, Infrastructure as Code, and large-scale AI infrastructure architectures
Experience with AI orchestration and agent frameworks such as LangGraph, CrewAI, MCP ecosystems, LlamaIndex, OpenAI Agents SDK, or similar runtime systems
Strong production coding skills in Python or Go
Experience building tooling, automation systems, deployment workflows, benchmarking frameworks, and operational platforms
Proven ability to benchmark and optimize AI infrastructure for scalability, reliability, GPU efficiency, runtime performance, latency optimization, and workload economics
Ability to establish technical credibility with CTOs, Principal architects, Product Engineering teams, and ecosystem partners
6+ years of experience in Forward Deployed Engineering, ML Engineering, Applied AI Engineering, AI Infrastructure, Technical Consulting, or equivalent customer-facing engineering roles supporting production AI systems
Experience building deployment standards, technical enablement programs, platform adoption frameworks, or ecosystem integration strategies
Active contribution to open-source AI, infrastructure, orchestration, or developer tooling ecosystems
Experience collaborating with GPU vendors, infrastructure providers, model vendors, or ecosystem partners on benchmarking, optimization, technical validation, or launch readiness initiatives

Benefits

Comp & perks

Reimbursement for relevant conferences, training, and education
Access to LinkedIn Learning's 10,000+ courses
Employee Assistance Program
Local Employee Meetups
Flexible time off policy
Bonus eligibility based on company and individual performance
Equity grants upon hire for eligible employees
Employee Stock Purchase Program for eligible employees