Senior Solutions Engineer, AI Infrastructure

🕒 il y a 5 jours

🇺🇸 États-Unis – Télétravail

⏰ Temps Plein

🟠 Senior

💻 Ingénieur Solutions

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501 - 1000 employés

💰 €83 000 000 Series D en 2021-05

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Description

• Lead technical discovery with customers across infrastructure, platform, ML, data, and executive stakeholders. • Design architectures for large-scale AI, HPC, analytics, and enterprise data workloads. • Help customers evaluate infrastructure involving GPUs, storage, networking, orchestration, and data movement. • Design and execute proofs of concept that validate performance, scale, reliability, and business value. • Translate complex technical requirements into clear solution designs, reference architectures, and deployment guidance. • Debug customer issues across Linux, storage, networking, Kubernetes, schedulers, GPUs, and application workloads. • Build technical assets, demos, runbooks, and field guidance for repeatable customer engagements. • Partner with sales on technical strategy, competitive positioning, and deal execution. • Partner with product and engineering to communicate customer requirements, gaps, and roadmap opportunities. • Help customers move from architecture design to production deployment.

🎯 Exigences

• 8 to 12+ years of technical experience, with significant hands-on infrastructure experience. • Experience building, operating, or architecting production platform infrastructure. • Strong understanding of Linux kernel implementation details, distributed systems including PAXOS and raft, storage implementations details like NAND or write amplification, networking store/forward, load balancing designs, and production operations. • Experience with one or more of: GPU infrastructure, large scale HPC systems, Kubernetes platforms from scratch, MLOps, storage systems, cloud infrastructure, data platforms, or large-scale enterprise infrastructure. • Ability to communicate credibly with engineers, architects, technical executives, and business stakeholders. • Strong discovery, problem-solving, and systems debugging skills. • Comfort operating in ambiguous, fast-moving environments. • Interest in customer-facing technical work, solution design, and business outcomes. • Experience with large-scale GPU clusters, distributed training, inference infrastructure, or AI platforms. • Experience with petabyte-scale storage or high-performance data systems. • Experience with Kubernetes, Slurm, Ray, Spark, or other orchestration / scheduling systems. • Domain Expertise with one or more of these - Lustre, Ceph, Weka, BeeGFS, GPFS, VAST, object storage, or distributed filesystems. • Experience with InfiniBand, RoCE, RDMA, high-performance Ethernet, or NVIDIA/Mellanox networking. • Direct Experience with CUDA, NCCL, DCGM, GPUDirect, checkpointing, dataset staging, or model-serving infrastructure. • Experience across multiple industries or customer environments.