Senior Forward Deployed Engineer II, AI/ML

This is a fully remote position, open to applicants in California, +1 more state.

📋 Description

• Collaborate with strategic AI-native enterprises and startups to design, implement, enhance, and scale production-level AI and agentic systems on DigitalOcean’s AI-Native Cloud.

• Assist with intricate migrations, production-ready proofs of concept, deployment acceleration, and the expansion of workloads over inference and runtime platforms.

• Improve distributed inference and runtime performance by utilizing benchmarking, GPU efficiency tuning, KV-cache optimization, speculative decoding, prefill/decode disaggregation, multi-node deployments, and latency/cost optimization.

• Serve as the initial customer for AI-native platform capabilities, which include the Inference Engine, runtimes, orchestration systems, GPU platforms, and deployment workflows.

• Highlight operational insights, architectural gaps, and scaling challenges to the Product Engineering and Research teams.

• Develop scalable deployment frameworks, benchmarking systems, automation tools, AI starter kits, operational playbooks, finetuning workflows, and reference architectures.

• Work together with GPU vendors, model providers, infrastructure partners, and ISVs on co-development, technical validation, optimization, and ensuring launch readiness.

• Equip customer-facing technical and partner teams with validated deployment patterns, benchmarking insights, operational playbooks, reference architectures, demos, and technical support.

• Be prepared to travel up to 30% for customer interactions, strategic onsite workshops, ecosystem partnerships, conferences, and internal collaboration.

• Propel production AI adoption and contribute to the development of DigitalOcean’s AI-Native Cloud for inference and agentic workloads.

⛳️ Requirements

• Proven experience in designing and operationalizing production AI systems, encompassing inference workloads, agentic runtimes, orchestration frameworks, and AI-native applications.

• Practical experience with inference and serving frameworks such as vLLM, SGLang, Ray Serve, NVIDIA Dynamo, llm-d, or their equivalents.

• Familiarity with LLM optimization techniques like continuous batching, quantization, KV-cache optimization, and speculative decoding.

• In-depth knowledge of NVIDIA and AMD GPU platforms and ecosystems, including CUDA, ROCm, TensorRT, Triton, NCCL, RCCL, NVLink, XGMI, and RoCE.

• Strong expertise in 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 tools.

• Proficient in production coding using Python or Go.

• Experience in developing tools, automation systems, deployment workflows, benchmarking frameworks, and operational platforms.

• Ability to benchmark and optimize AI infrastructure focusing on scalability, reliability, GPU efficiency, runtime performance, latency, and workload economics.

• Capacity 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 similar customer-facing engineering roles supporting production AI systems.

• Experience in developing deployment standards, technical enablement programs, platform adoption frameworks, or ecosystem integration strategies.

• Active involvement in open-source AI, infrastructure, orchestration, or developer tooling ecosystems.

• Experience collaborating with GPU vendors, infrastructure providers, model vendors, or ecosystem partners.

🏝️ Benefits

• Reimbursement for relevant conferences, training, and educational pursuits.

• Access to LinkedIn Learning's extensive library of over 10,000 courses.

• Employee Assistance Program.

• Local Employee Meetups.

• Flexible time-off policy.

• Bonus eligibility based on both company and individual performance.

• Equity compensation, including equity grants upon hiring.

• Employee Stock Purchase Program.

• Competitive benefits package, varying based on local regulations and preferences.