Senior Forward Deployed Engineer II (AI/ML)
DigitalOcean6 days ago
Base Salary
$167k - $209k/yr
Responsibilities
- Architect, deploy, optimize, and scale production AI and agentic workloads for strategic customers and partners.
- Optimize distributed inference and runtime performance through benchmarking, GPU tuning, caching, decoding, multi-node deployment, and latency or cost optimization.
- Validate AI-native platform capabilities and communicate operational insights, architectural gaps, and scaling bottlenecks to Product Engineering and Research teams.
- Build deployment frameworks, benchmarking systems, automation tooling, AI starter kits, operational playbooks, fine-tuning workflows, and reference architectures.
- Collaborate with GPU vendors, model providers, infrastructure partners, and ISVs on technical validation, optimization, co-development, and launch readiness.
- Enable customer-facing and partner teams with deployment patterns, benchmarking insights, demos, playbooks, and technical guidance.
- Travel up to 30% for customer engagements, workshops, partnerships, conferences, and internal collaboration.
Requirements
- 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 designing and operationalizing production inference workloads, agentic runtimes, orchestration frameworks, and AI-native applications.
- Hands-on experience with inference and serving frameworks such as vLLM, SGLang, Ray Serve, NVIDIA Dynamo, or llm-d, plus LLM optimization techniques.
- Deep expertise with NVIDIA and AMD GPU platforms and 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 LangGraph, CrewAI, MCP ecosystems, LlamaIndex, OpenAI Agents SDK, or similar AI orchestration and agent frameworks.
- Strong production coding skills in Python or Go and experience building tooling, automation, deployment workflows, benchmarking frameworks, and operational platforms.
- Ability to benchmark and optimize AI infrastructure for scalability, reliability, GPU efficiency, runtime performance, latency, and workload economics.
- Ability to establish technical credibility with CTOs, principal architects, Product Engineering teams, and ecosystem partners.
- Experience building deployment standards, technical enablement programs, platform adoption frameworks, or ecosystem integration strategies is preferred.
- Active open-source involvement and experience collaborating with GPU, infrastructure, model, or ecosystem vendors are preferred.
Benefits
- Remote role.
- Up to 30% travel for customer engagements and related activities.
- Reimbursement for relevant conferences, training, and education.
- Access to LinkedIn Learning courses.
- Employee Assistance Program, local employee meetups, and flexible time off.
- Potential bonus, equity compensation, equity grants upon hire, and Employee Stock Purchase Program.
Tech Stack
Categories
Forward Deployed
About DigitalOcean
DigitalOcean provides cloud infrastructure and platform services for developers, startups, and small to mid-sized businesses, including virtual machines (Droplets), managed Kubernetes and databases, object/block storage, networking, and GPUs for AI workloads. It operates a usage-based, self-service public cloud with APIs, CLI, and a marketplace to deploy and scale applications. Founded in 2012 and headquartered in Broomfield, Colorado, DigitalOcean is a public company listed on the NYSE.