{"id":554138,"url":"https://alion.io/job/cloudera-staff-system-engineer-ai-infrastructure","title":"Staff ML Inference Engineer","company":{"id":58503,"name":"Cloudera","domain":"cloudera.com","url":"https://alion.io/company/cloudera","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":92,"open_postings":16,"ghost_share":0,"stale_share":0.313,"repost_share":0,"time_to_fill_p50_days":25,"computed_at":"2026-10-25T05:49:26Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":95000,"max_usd":273000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":11},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Kubernetes","optional":false},{"name":"KV Cache","optional":false},{"name":"Linux","optional":false},{"name":"NVIDIA NIM","optional":false},{"name":"Python","optional":false},{"name":"Quantization","optional":false},{"name":"SGLang","optional":false},{"name":"TensorRT-LLM","optional":false},{"name":"Triton","optional":false},{"name":"vLLM","optional":false},{"name":"TensorRT","optional":true}],"status":"live","first_seen_at":"2026-09-08T19:15:11Z","employer_posted_date":"2026-10-14","last_verified_at":"2026-10-23T07:21:23Z","board_verified":false,"closed_at":null,"days_open":46,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":46},"description":"Business Area:\nProfessional ServicesSeniority Level:\nMid-Senior levelJob Description:\nAt 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.\nAs adoption of AI grows across our platform, customers are deploying models for increasingly diverse business workloads across on-premises, private, sovereign, and public-cloud environments. We are seeking a Staff ML Inference Engineer to develop and optimize production model-serving solutions through Cloudera AI Inference.\nThis is a hands-on ML engineering role focused on inference and performance. You will investigate workload requirements, benchmark model-serving configurations, identify bottlenecks, and develop validated solutions across different GPU environments. Working alongside platform and applied AI engineers, you will translate customer requirements into reliable, efficient model endpoints and turn technical findings into reusable engineering capabilities and product improvements.\nAs a Staff ML Engineer, you will:\nAI Inference & Model Serving: Deploy and optimize production model-serving solutions through Cloudera AI Inference across on-premises, private-cloud, and public-cloud environments. Validate model, runtime, and GPU compatibility, alongside API behavior, concurrency, and reliability.\nInference Benchmarking & Capacity Planning: Develop repeatable benchmarks using representative input lengths, output lengths, request rates, and concurrency. Recommend GPU types, counts, and serving configurations based on model characteristics, memory requirements, latency targets, throughput, and availability needs.\nInference & Performance Engineering: Diagnose bottlenecks across serving runtimes, GPU compute and memory, CPU processing, networking, and I/O. Evaluate supported optimization techniques such as batching, KV-cache management, parallelism, quantization, and replica scaling, validating their impact on performance and model quality.\nGPU Resource Efficiency: Evaluate inference workloads across different GPU generations and configurations. Improve resource utilization while balancing latency, throughput, cost, and operational constraints, including GPU sharing or partitioning where appropriate.\nInference Tooling & Validation: Build benchmarking, profiling, diagnostic, and validation tooling to reproduce customer scenarios and measure time to first token, inter-token latency, end-to-end latency, throughput, and GPU memory consumption.\nProduct & Engineering Collaboration: Work with customer teams, platform engineers, applied AI engineers, and core engineering to resolve serving challenges. Translate findings into product requirements, performance improvements, tested configurations, and reusable deployment guidance.\nWe’re excited about you if you have:\nEight or more years of engineering experience spanning ML systems, software development, distributed systems, or performance engineering, with evidence of technical leadership and independent ownership.\nPractical experience operating GPU-based model-serving workloads in production, using at least one inference stack such as NVIDIA NIM, vLLM, SGLang, TensorRT-LLM, Triton, or equivalent.\nThe ability to turn ambiguous requirements into testable hypotheses, investigate root causes, and validate solutions through experiments, profiling, and measurement.\nStrong programming skills in Python and a solid foundation in Linux, containers, distributed services, and Kubernetes, enabling you to investigate issues across the serving stack.\nDemonstrated experience benchmarking and improving inference performance, with an understanding of model weights, runtime overhead, KV-cache requirements, and latency-throughput trade-offs.\nExperience sizing GPU resources and serving configurations against workload demand, including context lengths, output lengths, concurrency, traffic bursts, and reliability requirements.\nYou may also have:\nExperience deploying open-weight LLMs or other AI models across different GPU configurations.\nExperience with multi-GPU or multi-node inference, including tensor parallelism and distributed workload orchestration.\nExperience with GPU sharing and partitioning, such as NVIDIA MIG, or quantization techniques and their impact on model quality.\nExperience investigating GPU interconnect or distributed communication bottlenecks, using profiling tools such as NVIDIA Nsight.\nExperience building reusable inference benchmarks, deployment automation, or diagnostic tools for enterprise environments, including restricted or air-gapped deployments.\nWhat you can expect from us:\nGenerous PTO Policy\n\nSupport work life balance with Unplugged Days\n\nFlexible WFH Policy\n\nMental & Physical Wellness programs\n\nPhone and Internet Reimbursement program\n\nAccess to Continued Career Development\n\nComprehensive Benefits and Competitive Packages\n\nPaid Volunteer Time\n\nEmployee Resource Groups\n\nEEO/VEVRAA\n#LI-RC1 #LI-Hybrid","description_format":"text","description_chars":5200,"description_truncated":false,"requirements":{"experience_years_min":8,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Internet reimbursement","Wellness"],"hiring_locations":[{"name":"Singapore","iso":"SG","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Query Engines & Big Data Processing","Data Warehouses & 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