Senior Forward Deployed Engineer II (AI/ML)
DigitalOcean4 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, KV-cache optimization, speculative decoding, prefill/decode disaggregation, and multi-node deployments.
- 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, finetuning workflows, operational playbooks, 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 through deployment patterns, demos, benchmarking insights, and technical guidance.
- Travel up to 30% for customer engagements, workshops, partnerships, conferences, and internal collaboration.
Requirements
- Hands-on experience designing and operationalizing production AI systems, inference workloads, agentic runtimes, orchestration frameworks, and AI-native applications.
- Strong experience with inference and serving frameworks such as vLLM, SGLang, Ray Serve, NVIDIA Dynamo, or llm-d, and 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, and OpenAI Agents SDK.
- Strong production coding skills in Python or Go and experience building tooling, automation, deployment workflows, benchmarking frameworks, and operational platforms.
- Proven 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 while managing strategic production deployments.
- At least 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 with platform enablement programs, deployment standards, platform adoption frameworks, ecosystem integrations, open-source AI or infrastructure contributions, and collaboration with GPU, infrastructure, model, or ecosystem vendors is preferred.
Benefits
- Remote role.
- Up to 30% travel for customer engagements, workshops, ecosystem partnerships, conferences, and internal collaboration.
- Reimbursement for relevant conferences, training, and education, plus 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.