Senior Director, Inference Products and Optimizations

About the role

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 Senior Director of Engineering to lead a high-performing engineering team building and scaling our Large Language Model (LLM) inference products.

Responsibilities

• Team Leadership & Development: Recruit, mentor, and coach engineers on the team, fostering a culture of ownership, technical excellence, and continuous improvement.
• Performant and Scalable Inference Products: Work with Product teams to define and execute on the Product roadmap for all of DigitalOcean’s Inference Products - including Serverless Inference, Dedicated Inference, Inference Router, Batch Inference and Multimodal Inference.
• Inference Optimizations and Model Architecture: Lead the design and evolution of our inference serving stack, driving deep technical strategy across vLLM, SGLang, and LLM-D to optimize throughput, latency, and GPU utilization at scale. Architect the model-serving and optimization layer — spanning quantization, KV-cache management, speculative decoding, and disaggregated serving — to deliver best-in-class performance-per-dollar across our LLM inference products.
• 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 to maintain the customer SLAs.
• 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.

Requirements

• Experience: 10+ years of software engineering experience, with 6+ years in a technical leadership or management role, ideally within Inference Systems or AI/ML systems.
• Technical Depth: Deep expertise in distributed systems design, modern AI/ML technologies, Kubernetes at scale, and LLM inference, and AI workload orchestration, scheduling, and resource management. Ability to engage in deep technical discussions with your team regarding highly scalable control plane design, inference engines (vLLM, SGLang), and model architectures.
• Hardware-Aware Optimization: Strategic knowledge of GPU architectures (NVIDIA and/or AMD), interconnects (like NVLink), and hardware topology and their direct impact on AI training and inference performance.
• Systems Engineering & Security: Familiarity with concepts in container runtime internals, system isolation, and security contexts to manage risk in shared infrastructure.
• Observability and SLOs: Expertise in defining, tracking, and operationalizing deep infrastructure and inference metrics (e.g., TTFT, TPOT) to drive performance improvements and meet service level objectives.

Qualifications

• Experience: 10+ years of software engineering experience, with 6+ years in a technical leadership or management role, ideally within Inference Systems or AI/ML systems.
• Technical Depth: Deep expertise in distributed systems design, modern AI/ML technologies, Kubernetes at scale, and LLM inference, and AI workload orchestration, scheduling, and resource management. Ability to engage in deep technical discussions with your team regarding highly scalable control plane design, inference engines (vLLM, SGLang), and model architectures.
• Hardware-Aware Optimization: Strategic knowledge of GPU architectures (NVIDIA and/or AMD), interconnects (like NVLink), and hardware topology and their direct impact on AI training and inference performance.
• Systems Engineering & Security: Familiarity with concepts in container runtime internals, system isolation, and security contexts to manage risk in shared infrastructure.
• Observability and SLOs: Expertise in defining, tracking, and operationalizing deep infrastructure and inference metrics (e.g., TTFT, TPOT) to drive performance improvements and meet service level objectives.

Skills

• Experience: 10+ years of software engineering experience, with 6+ years in a technical leadership or management role, ideally within Inference Systems or AI/ML systems.
• Technical Depth: Deep expertise in distributed systems design, modern AI/ML technologies, Kubernetes at scale, and LLM inference, and AI workload orchestration, scheduling, and resource management. Ability to engage in deep technical discussions with your team regarding highly scalable control plane design, inference engines (vLLM, SGLang), and model architectures.
• Hardware-Aware Optimization: Strategic knowledge of GPU architectures (NVIDIA and/or AMD), interconnects (like NVLink), and hardware topology and their direct impact on AI training and inference performance.
• Systems Engineering & Security: Familiarity with concepts in container runtime internals, system isolation, and security contexts to manage risk in shared infrastructure.
• Observability and SLOs: Expertise in defining, tracking, and operationalizing deep infrastructure and inference metrics (e.g., TTFT, TPOT) to drive performance improvements and meet service level objectives.

Benefits

• Competitive salary range: $274,400 - $343,000
• Flexible time off policy
• Employee Assistance Program
• Local Employee Meetups
• Reimbursement for relevant conferences, training, and education
• Access to LinkedIn Learning's 10,000+ courses
• Equity compensation to eligible employees, including equity grants upon hire and the option to participate in our Employee Stock Purchase Program

Pay

$274,400 - $343,000

Schedule

Remote role