Staff ML Inference Engineer

Job description

Business Area: Professional Services Seniority Level: Mid-Senior level Job Description: At Cloudera, we empower people to transform complex data into clear and actionable insights. With as much data under management as the hyperscalers, we're the preferred data partner for the top companies in almost every industry. Powered by the relentless innovation of the open source community, Cloudera advances digital transformation for the world’s largest enterprises. As adoption of AI grows across our data and AI platform, customers are using Cloudera AI for increasingly diverse workloads across on-premises, private, sovereign, and public-cloud environments. We are seeking a Staff Systems Engineer – AI Infrastructure to understand the infrastructure requirements and constraints behind these workloads and drive hands-on engineering solutions that improve our product. This is a hands-on system engineering role . You will investigate ambiguous technical scenarios, identify underlying systems constraints, and develop and validate solutions through experimentation, prototyping, benchmarking, and engineering work. Success means turning diverse workload and infrastructure scenarios into validated technical solutions and, where appropriate, scalable product capabilities and improvements. As a Staff System Engineer, you will: Work across AI Infrastructure & Systems Engineering : Investigate and design solutions for AI workloads across heterogeneous GPU environments, on-premises datacenters, private infrastructure, and public clouds; reproduce complex scenarios and validate solutions through hands-on experimentation and proof-of-concepts. Work across AI Workload & Inference Engineering : Develop and optimize infrastructure solutions for production AI workloads, including inference and serving, considering workload characteristics, performance, GPU capacity, resource utilization, and deployment constraints. Systems & Performance Engineering : Diagnose issues across Linux, GPU runtimes and drivers, containers, networking, storage, and hardware; identify root causes and validate solutions to performance, reliability, and scalability challenges. GPU Resource Efficiency : Investigate approaches for efficiently allocating and utilizing GPU resources across AI workloads, including workload-aware sharing and partitioning where appropriate. Infrastructure Tooling & Validation : Build diagnostic, benchmarking, deployment, and validation tooling to reproduce complex infrastructure scenarios and evaluate product performance across different environments. Product & Engineering Collaboration : Translate infrastructure findings into technical requirements, product improvements, performance optimizations, and reusable platform capabilities in partnership with product and engineering teams. We’re excited about you if you have: 8+ years of experience in systems software, distributed infrastructure, platform engineering, performance engineering, or a related field, with a track record of independently solving complex, ambiguous engineering problems. Hands-on experience with production GPU-based infrastructure supporting AI workloads, with a strong understanding of the infrastructure characteristics and constraints that affect them. Ability to take ambiguous problems, develop hypotheses, investigate root causes, and build or validate solutions through experimentation, debugging, prototyping, and measurement without requiring step-by-step direction. Strong understanding of distributed systems, Linux, containers, and production infrastructure, with the ability to reason across multiple layers of the technology stack. Demonstrated ability to diagnose and optimize bottlenecks involving GPU utilization, compute, memory, networking, I/O, or workload/runtime behavior. Hands-on experience designing or operating production infrastructure in on-premises, private-cloud, and/or public-cloud environments, with an understanding of the practical constraints of heterogeneous environments . You may also have: Experience with modern AI inference and serving technologies such as NVIDIA NIM, vLLM, SGLang, Triton, or equivalent. Experience with Kubernetes or distributed AI workload orchestration. Experience with GPU resource management, sharing, or partitioning, including technologies such as NVIDIA MIG. Experience diagnosing high-performance GPU networking or distributed communication issues. Experience building infrastructure diagnostics, benchmarks, or proof-of-concept systems to evaluate new architectures or technologies. What you can expect from us: Generous PTO Policy Support work life balance with Unplugged Days Flexible WFH Policy Mental & Physical Wellness programs Phone and Internet Reimbursement program Access to Continued Career Development Comprehensive Benefits and Competitive Packages Paid Volunteer Time Employee Resource Groups EEO/VEVRAA #LI-RC1