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 distribution using OCI Image Volumes and implement Checkpoint/Restore capabilities (via CRIU and NVIDIA cuda-checkpoint) for long-running training fault recovery.
- Security and Isolation: Define and enforce security best practices for AI workloads, ensuring multi-layered isolation environments and agent sandboxes are deployed to safely execute untrusted code (e.g., using Kata Containers, gVisor, or microVMs).
- Experience: 10+ years of software engineering experience, with 6+ years in a technical leadership or management role, ideally within AI/ML infrastructure or cloud platforms.
- 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 GPU programming (CUDA, ROCm), inference engines (vLLM, SGLang), and infrastructure at scale.
- 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.
- Product Mindset: Demonstrated ability to translate complex technical requirements into user-focused product features. Understanding of the balance between innovation and reliability.
- Communication: Excellent communication skills, with the ability to explain technical decisions to non-technical stakeholders and align diverse teams around a shared vision.
- Ownership: A strong sense of ownership and a proactive drive to identify and resolve issues preventing your team from delivering value.
*This job is located in Bengaluru, India
#LI-Hybrid
- We innovate with purpose. You’ll be a part of a cutting-edge technology company with an upward trajectory, who are proud to simplify cloud and AI so builders can spend more time creating software that changes the world. As a member of the team, you will be a Shark who thinks big, bold, and scrappy, like an owner with a bias for action and a powerful sense of responsibility for customers, products, employees, and decisions.
- We prioritize career development. At DO, you’ll do the best work of your career. You will work with some of the smartest and most interesting people in the industry. We are a high-performance organization that will always challenge you to think big. Our organizational development team will provide you with resources to ensure you keep growing. We provide employees with reimbursement for relevant conferences, training, and education. All employees have access to LinkedIn Learning's 10,000+ courses to support their continued growth and development.
- We care about your well-being. Regardless of your location, we will provide you with a competitive array of benefits to support you from our Employee Assistance Program to Local Employee Meetups to flexible time off policy, to name a few. While the philosophy around our benefits is the same worldwide, specific benefits may vary based on local regulations and preferences.
- We reward our employees. The salary range for this position is based on market data, relevant years of experience, and skills. You may qualify for a bonus in addition to base salary; bonus amounts are determined based on company and individual performance. We also provide equity compensation to eligible employees, including equity grants upon hire and the option to participate in our Employee Stock Purchase Program.
- DigitalOcean is an equal-opportunity employer. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.
Application Limit: You may apply to a maximum of 3 positions within any 180-day period. This policy promotes better role-candidate matching and encourages thoughtful applications where your qualifications align most strongly.
Skills Required
- 10+ years software engineering experience with 6+ years in technical leadership or management
- Deep expertise in distributed systems design and large-scale Kubernetes (1,000+ nodes) operations
- Strong knowledge of LLM inference, AI workload orchestration, scheduling, and resource management
- Hands-on familiarity with GPU programming and optimization (CUDA, ROCm)
- Experience with inference engines and related tooling (e.g., vLLM, SGLang, NVIDIA Grove)
- Experience implementing fractional GPU allocation, multi-scheduler architectures, and batch dispatching
- Knowledge of hardware-aware topology optimization (NVLink, PCIe, NUMA) and multi-GPU parallelization
- Experience with checkpoint/restore strategies and tooling (CRIU, NVIDIA cuda-checkpoint)
- Familiarity with container runtime isolation and sandboxing (Kata Containers, gVisor, microVMs)
- Proven ability to define and operationalize observability, SLOs, and performance metrics (e.g., TTFT, TPOT)
- Strong cross-functional communication, product mindset, and ownership of delivery
DigitalOcean Compensation & Benefits Highlights
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Healthcare Strength — Health coverage includes medical, dental, vision, and mental-health support plus employer-paid life/AD&D/disability, with multiple plan options and above-average employer contributions. Offerings are described as market-leading and in some cases fully paid to keep premiums low.
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Equity Value & Accessibility — Equity awards (new-hire and performance RSUs) are paired with an Employee Stock Purchase Plan offered at a discount. This ownership component augments cash compensation and broadens participation in company growth.
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Parental & Family Support — Paid parental leave is provided for a defined period and includes a structured, part-time transition-back program. The approach emphasizes a smoother return-to-work experience for new parents.
DigitalOcean Insights
What We Do
DigitalOcean is the Inference Cloud — a full-stack, production-ready cloud platform built to run AI applications with predictable performance, sustainable economics, and radically simpler operations at scale. We are built for teams turning AI into real products — not just training models. Our advantage is not fewer features, but fewer failure modes when operating AI at scale — combining minimal operational overhead, predictable cost efficiency, and a full-stack cloud that works as a system. Hyperscalers are broad by design. Neoclouds are infrastructure-first. DigitalOcean is inference-first — with a real cloud underneath. It combines inference-optimized compute, managed inference software, and integrated cloud capabilities that reduce operational burden for teams running real workloads. Inference is the foundation—not the boundary. Everything else builds on top of it.
Why Work With Us
At DO, we do career-defining work. We innovate with AI and build cutting-edge tech. Our rewards to match that intensity - to motivate you, recognize your impact, and give you what you need to thrive. If you have a growth mindset, like to think big and bold, and are energized by the fast-paced environment, you'll find your place here.
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Employees work remotely.
We commit to both remote work and in-person collaboration. These ways of working are dependent on specific roles and are mutually agreed upon by employees. In the US, we are mainly remote. In our APAC locations, we have a hybrid in-office approach.
