Senior ML Engineer - Kimchi (LLM Inference Optimization
Tasks
- Benchmark inference performance
- Define technical direction for inference optimization
- Improve KV cache utilization
- Instrument systems before optimization
- Optimize LLM inference throughput
- Quantize model weights and activations
- Reduce LLM latency
- Reduce model cold start time
- Scale distributed inference across nodes
Perks/Benefits
- Equipment budget
- Equity options
- Extra days off
- Hackathon
- Learning budget
- Personal project time
- Remote-first flexibility
- Team-building budget
Skills/Tech-stack
Activations quantization | Amazon Web Services | ArgoCD | CUDA | CUDA-adjacent tooling | Checkpointing | Chunked prefill | ClickHouse | Cloud Pub/Sub | Cloud platform | Collective communication | Continuous batching | Distributed Systems | Eviction Policy | FP8 | GitLab CI | Google Cloud | Google Cloud Platform | Google Cloud Pub/Sub | Grafana | INT4) | INT8 | KV cache | KV quantization | Kernel tuning | Kubernetes | Kv cache reuse | Loki | Microsoft Azure | Multi-GPU | Multi-node | Network aware placement | Paged Attention | PostgreSQL | Prefix caching | Prometheus | Pub/Sub | PyTorch | Python | Quantization | SGLang | Sharding | Speculative decoding | Tempo | TensorRT-LLM | VLLM | Web Services | Weights quantization
Education
N/A
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