AI Engineer

Responsibilities:

• Own technical success for training-cluster customers from discovery to production.
• Understand the customer's training workload: framework, model size, data pipeline, cluster size, GPU type, networking needs, storage needs, checkpointing strategy, and reliability expectations.
• Recommend cluster architecture across Kubernetes, Slurm, Ray, containers, storage, networking, and observability.
• Help customers run pilots, validate cluster performance, debug jobs, and move toward production usage.
• Debug issues across NCCL, networking, storage, scheduler behavior, containers, drivers, distributed training frameworks, checkpointing, and job failures.
• Create reusable playbooks, reference architectures, examples, and troubleshooting guides.
• Bring high-signal product feedback to platform engineers: what customers are trying to train, what breaks, what is confusing, and what Jarvis Labs should improve.
• Support pre-sales technical evaluation and post-sales workload success, without owning revenue quota.

Requirements:

• Strong technical background in ML engineering, platform engineering, solutions architecture, or production training workloads.
• Practical understanding of distributed training or large-scale compute workloads. Comfortable with Kubernetes, Linux, containers, networking, storage, logs, metrics, and debugging.
• Ability to communicate clearly with ML engineers, platform teams, and customer stakeholders.
• High agency: you solve customer problems, synthesize patterns, and improve the system.
• Strong written communication for customer notes, internal feedback, runbooks, and examples.

Strong Pluses:

• Experience with Slurm, Ray, Kubernetes, Argo, Kubeflow, Volcano, Kueue, or similar systems.
• Experience with PyTorch DDP/FSDP, DeepSpeed, Megatron-LM, NCCL, checkpointing, data loading, or multi-node training.
• Experience with InfiniBand/RDMA, GPU topology, storage bottlenecks, or HPC operations.
• Experience as an ML engineer who has actually trained or fine-tuned models on cloud GPU infrastructure.
• Ability to write scripts/tools to automate setup, validation, benchmarking, and debugging.

Skills

K8s, Kubernetes, ML, distributed, distributed training, machine learning