Senior ML Engineer - Kimchi (LLM Inference Optimization)
Tasks
- Define inference optimization technical direction
- Design distributed inference topologies
- Implement quantization for LLMs
- Improve KV cache utilization
- Optimize LLM inference throughput
- Profile and fix inference bottlenecks
- Reduce LLM inference latency
- Reduce model cold start time
- Scale distributed inference across nodes
Perks/Benefits
- Annual hackathon
- Conference access
- Equipment budget
- Equity options
- Extra days off
- Learning budget
- Personal project time
- Remote-first environment
- Team-building budget
- Work-life balance
Skills/Tech-stack
AWS | ArgoCD | Azure | CUDA | Chunked prefill | ClickHouse | Collective communication | Continuous batching | Distributed Systems | FP8 | GCP PubSub | GitLab CI | Grafana | INT4) | INT8 | KV cache | Kernel tuning | Kubernetes | Loki | Paged Attention | PostgreSQL | Prefix caching | Prometheus | PyTorch | Python | Quantization | SGLang | Sharding | Speculative decoding | Tempo | TensorRT-LLM | VLLM
Education
N/A
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