Job Opportunities API

Dive in and do the best work of your career at DigitalOcean. Journey alongside a strong community of top talent who are relentless in their drive to build the simplest scalable cloud. If you have a growth mindset, naturally like to think big and bold, and are energized by the fast-paced environment of a true industry disruptor, you’ll find your place here.  We value winning together—while learning, having fun, and making a profound difference for the dreamers and builders in the world.  Our Inference Engine organization is seeking an experienced Engineering Director to lead a high-performing team of engineers as they design, develop, and scale our Large Language Model (LLM) inference platform across the serving, orchestration, and hosting layers. This team is at the heart of our mission to bring DigitalOcean’s famed simplicity to the world of optimized LLM inference. In this role, you will bridge the gap between product strategy and technical execution, fostering a culture of excellence while delivering robust systems that handle millions of users across the globe. What You'll Do: Team Leadership & Development: Recruit, mentor, and coach engineers on the team, fostering a culture of ownership, technical excellence, and continuous improvement. Execution & Delivery: Own the team's project execution, translating high-level business goals into clear technical roadmaps, measurable milestones, and successful, on-time delivery. Cross-Functional Partnership: Collaborate with Product Management, other engineering teams, and key stakeholders to align priorities, manage dependencies, and communicate progress and risks. Operational Health: Ensure the production health, stability, and on-call rotation of all services owned by the Inference Orchestration team. Oversee System Design: In partnership with the technical leads on your team, guide the architecture and implementation of a distributed inference platform optimized for diverse GPU platforms (NVIDIA and AMD). Ensure the platform is performant, scalable, and reliable. Champion Best Practices: Institutionalize benchmarking frameworks, observability, and auto-tuning capabilities to guide system and infrastructure tuning efforts. Encourage contributions to open-source inference engines to advance our capabilities. Strategic Architecture & Planning: Define the technical roadmap and oversee the architecture of high-throughput scheduling systems for massive Kubernetes clusters (1,000+ nodes, 10,000+ pods), focusing on scalability techniques like multi-scheduler architectures and batch dispatching. Maximize GPU Utilization : Engineer solutions for complex performance issues, including attention layer optimizations, memory and precision management, and advanced parallelization across multi-node GPU clusters. Eliminate GPU waste in multi-tenant environments by implementing fractional GPU allocation, leveraging mechanisms like KAI-Scheduler's Reservation Pods or hard-isolation tools like HAMi, and configuring time-based fairshare scheduling to balance over-quota pool access. Orchestrate Complex Inference : Implement and manage disaggregated AI inference pipelines using frameworks like NVIDIA Grove, coordinating multicomponent deployments (e.g., prefill leaders, decode workers, KV routers) with multilevel autoscaling and explicit startup ordering. Optimize Placement & Topology : Deploy topology-aware scheduling to align pod placement with physical hardware dimensions, such as NVLink connections, PCIe lanes, and NUMA nodes, minimizing communication latency for multi-GPU operations. Platform Performance & Reliability: Drive initiatives to enhance overall cluster performance, including optimizing scheduling latency, API server load, and implementing fault tolerance mechanisms like Checkpoint/Restore for long-running AI training jobs. Manage AI Storage & Fault Tolerance: Orchestrate efficient model weight dis