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As a Senior Forward Deployed Engineer II at DigitalOcean, you will join a dynamic team dedicated to revolutionizing cloud computing and AI. This role will be part of our pre-sales engagement motion, working directly with strategic customers, Sales, Product, and Engineering to help customers validate DigitalOcean for production AI workloads.

You will be a hands-on technical partner for customers evaluating DigitalOcean's AI products, helping with inference engine optimization, configuration and performance tuning, POCs, benchmarks, endpoint optimization, and production-readiness planning. The ideal person will become a trusted technical advisor to strategic AI customers and a key field voice helping shape DigitalOcean's inference roadmap, product feedback loop, and customer adoption strategy.

What You'll Do:

• Lead strategic pre-sales AI engagements: Partner with Sales to help customers evaluate DigitalOcean for production AI workloads, including serverless inference, dedicated inference, model deployment, and GPU-backed infrastructure.
• Optimize inference engines: Select, configure, and optimize inference engines based on hardware, model architecture, workload profile, latency requirements, and throughput targets.
• Drive POCs and benchmarks: Develop configuration updates and technical approaches to win critical POCs, improve benchmark outcomes, and accelerate customer validation.
• Tune production deployments: Optimize endpoint configurations, KV cache behavior, batching strategy, speculative decoding, tensor parallelism, quantization, GPU utilization, latency, throughput, and cost efficiency.
• Support customer architecture decisions: Help customers design scalable AI architectures across inference endpoints, Kubernetes, networking, observability, storage, and production deployment patterns.
• Remove technical blockers: Act as a hands-on technical partner during strategic customer evaluations, debugging issues, reviewing configurations, building prototypes, and helping customers move from evaluation to production.
• Scale field knowledge: Build reusable architectures, playbooks, demos, reference implementations, and technical guidance that help the broader organization support AI customers more effectively.

Key Metrics: 

• Strategic AI POC Win Rate: Percentage of FDE-supported AI POCs, benchmarks, or technical evaluations that convert to closed-won or approved production deployment.
• Time to Technical Validation: Reduction in time required for customers to validate serverless inference, dedicated inference, or GPU-backed workloads.
• Inference Optimization Impact: Measurable improvement in latency, throughput, GPU utilization, reliability, or cost efficiency across customer deployments.
• Product & Platform Feedback Impact: Number of high-quality customer requirements, platform gaps, or roadmap inputs surfaced from strategic engagements that lead to product improvements, fixes, or prioritized roadmap items.

What You'll Add to DigitalOcean:

• Experience: 5+ years in a technical role focused on AI infrastructure, inference systems, open-source LLM deployment, or model optimization.
• Inference Engine Depth: Hands-on expertise with inference engines such as vLLM, TensorRT-LLM, or SGLang, with the ability to debug and resolve performance issues.
• Inference Optimization: Strong knowledge of KV cache tuning, speculative decoding, tensor/pipeline parallelism, batching, and quantization.
• Post-Training Knowledge: Experience with fine-tuning or post-training workflows such as LoRA, SFT, DPO, RLHF, or GRPO.
• Model Landscape Awareness: Strong understanding of open-source models and how to select the right model based on use case, hardware, and performance goals.
• Coding Proficiency: Strong Python skills and comfort working in production environments.

• Base: $167,200 - $209,000

*This is a remote role

JR: 2026-7695

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