Shape the architecture of Netflix's large-scale ML training platform built on Kubernetes, Ray, and PyTorch

This role builds and operates the platform powering large-scale ML model training, fine-tuning, and evaluation across Netflix, on infrastructure built on Kubernetes, Ray clusters, and PyTorch distributed training primitives. It requires deep distributed-training expertise plus the ability to lead technical discussions and align ML engineers, researchers, and infra teams around platform direction.

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What this interview tests

ML training platform architecture (Kubernetes, Ray, PyTorch distributed primitives)
Diagnosing and optimizing large distributed training jobs (GPU utilization, memory, communication overhead, checkpointing, fault tolerance)
API/SDK design for both expert and non-expert ML practitioners
Cross-team technical leadership and stakeholder alignment
Foundation model training, fine-tuning, and distillation workflows
Operational excellence: observability, logging, on-call for training infra

Common question themes

Design a platform to power large-scale training, fine-tuning, and evaluation across an entire company

Diagnose a slow or unreliable distributed training job — walk through your process

Design easy-to-use training platform APIs for both experts and non-experts

Describe leading a design review or aligning cross-functional stakeholders on platform direction

How would you approach fault tolerance and checkpointing at scale

Experience with parallelism techniques (FSDP, tensor/pipeline) for scaling training

How candidates describe it

Real Software Engineer 5 – Training Platform interview stories — retold from candidates' public write-ups, with sources.

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