Top Tech Jobs & Startup Jobs

Lead and coordinate complex, cross-functional compute hardware and software programs (kernel, virtualization, drivers, firmware, GPUs). Manage dependencies, risks, execution, deployment automation, validation, observability, and customer/vendor interactions to scale large GPU/HPC cloud infrastructure.

Design, train, and deploy retrieval, reranking, and relevance ML models for a production agent-native search platform. Build embedding-based indexing and large-scale retrieval systems, define evaluation metrics and pipelines, optimize latency/quality/cost trade-offs, and collaborate with engineers to integrate models into 24x7 production services.

Design, implement, and operate the Virtual Private Cloud control and data planes. Improve performance, reliability, automation, and cross-service interactions. Develop networking features, perform load testing, and write high-performance code for cloud networking services.

Lead hands-on applied AI work: prototype demos, accelerate customer POC-to-production, perform technical onboarding, optimize distributed training and inference on GPU infrastructure, research and implement emerging ML techniques, and feed actionable product feedback while producing reusable assets and benchmarks.

Lead reliability and observability for compute nodes running VMs. Debug Linux user/kernel issues, troubleshoot CPU/memory/NUMA/cgroups, operate QEMU/KVM and container tech, design node-level metrics/logs/traces/SLIs/SLOs, run incident response and collaborate across platform, kernel, GPU and infrastructure teams.

The Senior Network Engineer will design, manage, and optimize large-scale data center networks for AI workloads, ensuring reliability and performance.

Lead benchmarking and profiling of GPU platforms for ML/AI workloads. Profile system and kernel-level GPU performance, debug and optimize ML training and inference, perform acceptance testing of GPU clusters, run experiments across GPU configurations and interconnects, and develop tooling and dashboards to visualize performance and bottlenecks.

Lead performance analysis and optimization of large-scale GPU/HPC clusters across hardware and software stacks. Troubleshoot real workloads, qualify and integrate new hardware, tune system configurations (networking, virtualization, kernel), support escalations, and collaborate with infrastructure teams and hardware vendors to ensure cluster performance meets expectations.

Collaborate on mechanical designs for data center systems, validating performance, overseeing construction, and ensuring designs meet quality benchmarks.

Maintain and scale DevTools SRE systems: improve user-facing developer workflows, build fault-tolerant self-healing architecture, optimize performance, modify open- and closed-source tools (GitLab, TeamCity), and support users while measuring improvements via metrics.