Senior AI Systems Engineer – Distributed Training & GPU Optimization

We are hiring an engineer who has personally built and optimized distributed training systems for large AI models and has deep, real-world experience optimizing GPU workloads specifically on Google Cloud.

This is not a research role, not a general ML engineer role, and not cloud-agnostic.

Core Responsibilities

Distributed Training (Foundation-Scale)

• Build and operate multi-node, multi-GPU distributed training systems (16–128+ GPUs).
• Implement and tune:
• PyTorch Distributed (DDP, FSDP, TorchElastic)
• DeepSpeed (ZeRO-2 / ZeRO-3, CPU/NVMe offload)
• Hybrid parallelism (data, tensor, pipeline)
• Create reusable distributed training frameworks and templates for large models.
• Handle checkpoint sharding, failure recovery, and elastic scaling.

GPU Optimization (Google Cloud Only)

• Optimize GPU utilization and cost on Google Cloud GPUs:
• A100, H100, L4
• Achieve high utilization through:
• Mixed precision (FP16 / BF16)
• Gradient checkpointing
• Memory optimization and recomputation
• Tune NCCL communication (All-Reduce, All-Gather) for multi-node GCP clusters.
• Reduce GPU idle time and cost per training run.

Google Cloud Execution

• Run and optimize training jobs using:
• Vertex AI custom training
• GKE with GPU node pools
• Compute Engine GPU VMs
• Optimize GPU scheduling, scaling, and placement.
• Use preemptible GPUs safely for large training jobs.

Performance Profiling

• Profile and debug GPU workloads using:
• NVIDIA Nsight Systems / Compute
• DCGM
• Identify compute, memory, and communication bottlenecks.
• Produce performance benchmarks and optimization reports.

Required Experience (Recruiter Screening Criteria)

Must-Have Experience (Non-Negotiable)

• 8+ years in ML systems, distributed systems, or HPC
• Hands-on experience scaling multi-node GPU training (16+ GPUs)
• Deep expertise in:
• PyTorch Distributed
• DeepSpeed
• NCCL
• Direct production experience on Google Cloud GPUs
• Proven record of GPU performance and cost optimization
• Strongly Preferred
• Experience training foundation models / LLM-scale models
• Experience with Vertex AI + GKE
• Experience optimizing GPU workloads at enterprise scale