Senior Forward Deployed Engineer II – AI/ML

This is a fully remote position, open to applicants in Massachusetts.

📋 Description

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

• Assist with complex migrations, production-ready proofs of concept, acceleration of deployments, and long-term workload expansion across inference and runtime platforms.

• Enhance 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.

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

• Identify operational insights, architectural deficiencies, and scaling constraints to Product Engineering and Research teams.

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

• Work alongside GPU vendors, model providers, infrastructure partners, and ISVs on co-development, technical validation, optimization, and preparation for launch.

• Empower customer-facing technical teams and partner teams through deployment patterns, benchmarking insights, operational playbooks, reference architectures, demonstrations, and technical guidance.

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

• Promote the production adoption of AI-native systems and contribute to the evolution of DigitalOcean’s AI-Native Cloud for inference and agentic workloads.

⛳️ Requirements

• Demonstrated experience in 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 comparable systems.

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

• Extensive expertise with NVIDIA and AMD GPU platforms and software ecosystems, including CUDA, ROCm, TensorRT, Triton, NCCL, RCCL, NVLink, XGMI, and RoCE.

• Strong proficiency 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 runtime systems.

• Solid production coding skills in either Python or Go.

• Proven experience in building tools, automation systems, deployment workflows, benchmarking frameworks, and operational platforms.

• Established ability to benchmark and optimize AI infrastructure for scalability, reliability, GPU efficiency, runtime performance, latency optimization, and workload economics.

• Capability to establish technical credibility with CTOs, Principal architects, Product Engineering teams, and ecosystem partners.

• Over 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 creating deployment standards, technical enablement programs, platform adoption frameworks, or ecosystem integration strategies.

• Active participation in 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

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

• Access to over 10,000 courses on LinkedIn Learning.

• Employee Assistance Program.

• Local Employee Meetups.

• Flexible time off policy.

• Eligibility for bonuses based on company and individual performance.

• Equity grants upon hire for eligible employees.

• Employee Stock Purchase Program available for eligible employees.