Senior Forward Deployed Engineer II – AI/ML

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

📋 Description

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

• Assist with complex migrations, production-ready proofs of concept, deployment acceleration, and long-term workload growth 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 optimizing latency/cost.

• Serve as the primary user for AI-native platform capabilities, including Inference Engine, runtimes, orchestration systems, GPU platforms, and deployment workflows.

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

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

• Partner with GPU vendors, model providers, infrastructure collaborators, and ISVs for co-development, technical validation, optimization, and readiness for launch.

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

• Travel up to 30% for customer engagements, strategic onsite workshops, ecosystem collaborations, conferences, and internal cooperation.

• Achieve outcomes measured by production workload launches, reduced time-to-production, pilot-to-production conversions, platform adoption, product enhancements, roadmap influence, tooling adoption, and ecosystem enablement.

⛳️ 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 comparable technologies.

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

• Extensive 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 solutions, 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 platforms.

• Proficient in production coding with Python or Go.

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

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

• Capacity 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 developing 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 education.

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

• Employee Assistance Program.

• Local Employee Meetups.

• Flexible time-off policy.

• Bonus eligibility based on company and individual performance.

• Equity compensation, including equity grants at the time of hire.

• Option to participate in the Employee Stock Purchase Program.

• Competitive benefits that vary based on local regulations and preferences.