About The Role

Introducing Moonlake, AI for creating real-time interactive content
Mission: Improve Throughput, Latency, & Cost - deploying our models 2–10× faster & cheaper without quality regressions.

GPU performance: CUDA/Triton kernels, FlashAttention family, paged attention, CUDA Graphs.
Serving stack: TensorRT-LLM/Triton Inference Server, vLLM/TGI; continuous batching; on-GPU KV reuse; speculative decoding/medusa; mixture-of-agents routing.
Parallelism: FSDP/ZeRO, TP/PP/expert parallel; NCCL tuning.
Quantization/PEFT: AWQ/GPTQ/FP8; LoRA/DoRA serving.
Systems: Ray/k8s/Argo, observability (Prom/Grafana/OpenTelemetry), autoscaling, A/B infra, canary + rollback.

Previous experience at Infra-heavy startups such as Databricks, Roblox
We are committed to being an on-site, in-person team currently based in San Mateo

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Member of Technical Staff - ML Infrastructure & Performance