Lead onboarding for customers deploying GPU clusters (bare metal, VMs, or hybrid).
Advise on cluster design: multi-GPU topology, NVLink/NVSwitch considerations, RDMA, Infiniband and RoCE Ethernet, networking throughput, and storage IOPS requirements.
Guide customers in selecting GPU types and configurations based on workload (training, fine-tuning, inference, embeddings, RAG pipelines).
Support distributed frameworks: PyTorch, TensorFlow, DeepSpeed, Megatron, JAX, Ray, Mosaic, HuggingFace, etc.
Advanced hands on Kubernetes skills
Advanced hands on SLURM skills
Identify bottlenecks (network, storage, memory bandwidth).
Provide tuning recommendations for batch size, mixed precision, parallelization strategies, and checkpointing.
Help customers evaluate cost vs. performance tradeoffs (GPU mix, CPU pairing, instance types, cluster sizing).
Own the long-term technical strategy across assigned GPU/AI accounts, including hyperscalers, labs, and high-growth AI startups.
Host recurring technical review meetings, roadmap reviews, and optimization sessions.
Define scaling plans, future GPU reservation needs, and capacity forecasting.
Partner with Support, SRE, Networking, NOC, and Product Management & Engineering to resolve high-urgency incidents.
Manage outage communications, corrective action plans, and postmortem reviews with customers.
Advocate for GPU reliability improvements and influence roadmap priorities.
Identify opportunities for expanded clusters, high speed storage, or networking upgrades.
Support Sales with technical validation and architecture diagrams needed for expansion.
Provide structured feedback on existing and future GPU offerings, networking fabrics, storage platforms, and upcoming AI/ML platform features.
Partner with Product on early access programs (new GPUs, pipelines, orchestration, etc.).

Requirements

2–5+ years as an AI/ML Engineer, AI/ML Ops, Technical Account Manager, HPC Engineer, Sales/Solutions Engineer or relevant technical role.
Strong knowledge of GPU hardware architectures (NVIDIA/AMD), CUDA/ROCm, distributed training, and ML frameworks.
Experience with Linux tuning, networking (Infiniband, RoCE fabrics).
Experience with high-performance storage systems (DDN, NetApp, Vast, Weka, etc.).
Ability to communicate complex concepts clearly to both executives and engineering teams.
Prior experience supporting hyperscale, AI labs, or large cluster deployments is a plus.
Cloud Native Computing Foundation Certified Kubernetes Administrator (CKA) certification is a plus.

Benefits

100% company-paid insurance premiums for employee medical, dental and vision plans.
401(k) plan that matches 100% up to 4%, with immediate vesting
Professional Development Reimbursement of $2,500 each year
11 Holidays + Paid Time Off Accrual + Rollover Plan
Increased PTO at 3 year and 10 year anniversary + 1 month paid sabbatical every 5 years + Anniversary Bonus each year
$500 stipend for remote office setup in first year + $400 each following year
Internet reimbursement up to $75 per month
Gym membership reimbursement up to $50 per month
Company paid Wellable subscription

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

GPU clustersmulti-GPU topologyNVLinkRDMAInfinibandRoCE EthernetKubernetesSLURMCUDALinux tuning

Soft Skills

communicationtechnical strategyproblem-solvingcollaborationcustomer supportfeedback provisionadvocacycapacity forecastingincident managementroadmap review

Certifications

Cloud Native Computing Foundation Certified Kubernetes Administrator (CKA)