Build the infrastructure that trains, aligns, and serves Netflix's most critical ML models

Netflix's Model Runtime team owns the systems behind training, alignment, and serving of Netflix's core ML models, spanning RL-based post-training, distributed training at scale, and next-gen GenAI inference. This role sits at the intersection of systems engineering and ML, working across the stack from PyTorch internals down to GPU kernels.

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What this interview tests

Distributed training systems (FSDP, tensor/pipeline/context parallelism)
RL-based post-training infra (GRPO, DPO, PPO, reward modeling)
GPU performance profiling and optimization (CUDA, NCCL, Nsight, PyTorch profiler)
Inference optimization (vLLM, TensorRT, quantization, KV-cache, continuous batching)
Multimodal/diffusion model serving and generation pipelines
Operational excellence: observability, logging, on-call for ML infra

Common question themes

Design a fault-tolerant distributed training system across hundreds of GPUs

How would you build RL/DPO-style post-training infra for a recommendation model

Describe a time you profiled and optimized a training or inference workload from framework down to kernel level

How do you evaluate new hardware/accelerators or frameworks for an ML infra stack

Trade-offs between real-time, near-real-time, and batch inference for GenAI workloads

How do you operate a small, highly autonomous infra team with outsized impact

How candidates describe it

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

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