Top Tech Jobs & Startup Jobs

Own and optimize the performance-critical LLM serving runtime: scheduling, batching, KV cache, speculative decoding, long-context and streaming. Tune and operate runtimes (vLLM, Dynamo, TensorRT-LLM, etc.), profile GPU/network/tokenizer bottlenecks, lead model onboarding (parallelism, quantization, context), define runtime playbooks, and partner with SRE and performance teams to deploy production improvements for latency, throughput, and cost efficiency.

The Power Quality Engineer supports global data centers by monitoring and optimizing power quality, reducing equipment failures, and ensuring compliance with international standards.

The Enterprise Systems Consultant will support daily operations of business systems, manage system-related requests, assist in implementation activities, and enhance business processes.

The Finance Business Partner will assist in budgeting, forecasting, financial performance analysis, contract reviews, and stakeholder support while ensuring compliance and managing risk.

Design, deploy, and operate production Kubernetes control planes for large GPU clusters. Implement GPU-specific scheduling, CRDs, multi-tenant isolation, BMaaS provisioning, Terraform-based IaC, monitoring, SLI/SLOs, and automated remediation workflows to enable autonomous AIOps-driven recovery and tenant self-service.

Front-line SRE covering 8AM–8PM PST monitoring GPU clusters, networking, storage, and sensors. Execute runbooks, perform hardware triage and physical DC tasks (rack, cable, swap), collect diagnostics for escalation, manage tickets (ServiceNow/Jira), update runbooks, and tag incidents to train the AIOps platform.

Deploy, operate, and optimize high-performance parallel/distributed storage systems for AI training and inference; implement multi-tenant isolation and GPU Direct data paths; instrument telemetry for storage-fault prediction; convert incidents into automated runbook-as-code; plan capacity, firmware, migrations, and DR for GPU clusters.

Lead design and operation of CI/CD and MLOps pipelines, cloud-native infrastructure, and observability. Own incident response, security/compliance, IaC, Kubernetes/Docker cluster management, GPU provisioning for AI workloads, high-availability and disaster recovery, and cross-team platform engineering to improve developer productivity and reliability.

Monitor GPU cluster, network, storage and environmental systems; respond to alerts and follow runbooks; perform hardware triage and standard remediation; collect diagnostics for escalation; manage incident tickets; perform physical data-center tasks (rack, cabling, hardware swaps); execute shift handoffs and maintain runbooks; assist with deployments and firmware under SME guidance.

Design and build global control-plane services for a GPU/AI cloud: tenant management, quota, metering/billing, RBAC, ticketing, customer portal/API, automation, region-aware scheduling, and operational tooling for detection and recovery.