Cloud Site Reliability Engineer

SambaNova Systems • San Jose, CA

21h • Hybrid

About The Position

The era of pervasive AI has arrived. In this era, organizations will use generative AI to unlock hidden value in their data, accelerate processes, reduce costs, drive efficiency and innovation to fundamentally transform their businesses and operations at scale. About SambaNova Systems Join the company that's building the future of AI computing. At SambaNova, we are disrupting the AI and high-performance computing space with our integrated hardware and software platform. Our DataScale systems and SambaFlow software are pushing the boundaries of what's possible with generative AI and large language models. We are a team of passionate innovators tackling some of the world's most challenging computational problems. The Role As a Cloud Site Reliability Engineer (SRE) specializing in our AI Inferencing Service, you will be the guardian of its reliability, performance, and scalability. You will bridge the gap between software development and operations, applying an engineering mindset to solve operational challenges. Your primary focus will be ensuring our inference endpoints have exceptional uptime, low-latency response times, and efficient resource utilization, directly impacting the experience of our customers and the success of our AI products. This role includes participating in a shared on-call rotation to maintain 24/7 service reliability.

Requirements

• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
• 3-5+ years of experience in a Site Reliability Engineer, DevOps, or related role supporting a large-scale, customer-facing service in a public cloud environment (AWS, GCP, Azure).
• Strong programming/scripting skills in languages like Python, Go, or Java.
• Proven experience with containerization and orchestration technologies (Docker, Kubernetes).
• Deep understanding of monitoring and observability principles and tools (e.g., Prometheus, Grafana, ELK Stack, Datadog).
• Solid experience with Infrastructure as Code (e.g., Terraform, CloudFormation).
• Familiarity with CI/CD principles and tools (e.g., Jenkins, GitHub Actions, ArgoCD).
• Excellent problem-solving skills and a systematic approach to troubleshooting complex distributed systems.

Nice To Haves

• Experience in a hybrid environment bridging cloud and on-premise/data center infrastructure.
• Direct experience supporting ML/AI inferencing services in production.
• Familiarity with GPU-accelerated computing and optimizing workloads for NVIDIA GPUs for purposes of mapping to RDUs.
• Knowledge of model serving frameworks like vLLM, SGLang or Ray.
• Understanding of MLOps principles and practices.
• Experience with managing and tuning databases (SQL or NoSQL) and caching systems (Redis, Memcached).
• Strong Linux/Unix system administration fundamentals.

Responsibilities

• Take shared ownership of the production inferencing service, including its availability, latency, performance, efficiency, change management, monitoring, emergency response, and capacity planning across multiple regions.
• Implement and support AI infrastructure in new regions, such as Asia, Europe, and Latin America, to support the growth of our business.
• Participate in a balanced on-call rotation to provide 24/7 support for the service.
• Lead the response to incidents affecting the inferencing service, driving blameless post-mortems and implementing corrective actions to prevent recurrence.
• Develop and maintain advanced monitoring, alerting, and dashboarding (using tools like Prometheus, Grafana, Datadog) to gain deep insights into service health, model performance (e.g., latency, throughput, error rates), and accelerator utilization.
• Ensure alerts are actionable and have a low false-positive rate, minimizing on-call fatigue.
• Proactively identify and eliminate performance bottlenecks.
• Design and implement auto-scaling policies to handle variable inference loads cost-effectively.
• Use insights from on-call incidents to drive improvements that enhance system stability and scalability.
• Manage and evolve our cloud infrastructure (on AWS, GCP, and/or Azure along with on-prem) using tools like Terraform and Ansible, ensuring it is secure, repeatable, and scalable.
• Champion automation by building and improving CI/CD pipelines for the seamless and safe deployment of new model versions and service updates.
• Automate manual toil identified during on-call shifts, reducing future operational overhead.
• Forecast infrastructure needs based on product roadmaps and usage trends.
• Work with finance and engineering teams to manage cloud costs and optimize spending.
• Define, measure, and report on Service Level Objectives (SLOs) and Indicators (SLIs) for the inferencing platform, using data to drive prioritization and reliability investments.

Benefits

• Competitive Compensation
• Equity
• Excellent benefits
• Flexible work environment
• 95% premium coverage for employee medical insurance
• 77% premium coverage for dependents
• Health Savings Account (HSA) with employer contribution
• Dental insurance
• Vision insurance
• Short/Long term Disability insurance
• Basic Life insurance
• Voluntary Life insurance
• AD&D insurance
• Flexible Spending Account (FSA) options like Health Care, Limited Purpose, and Dependent Care
• Full subscription to Headspace
• Gympass+ membership with access to physical gyms
• One Medical membership
• Counseling services with an Employee Assistance Program

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 101-250 employees