Machine Learning Engineer — Inference Optimization
Tasks
- Benchmark model performance across hardware and cloud
- Build and maintain inference serving infrastructure
- Implement quantization and KV cache optimization
- Implement speculative decoding and batching strategies
- Improve reliability monitoring and observability
- Optimize machine learning inference systems for latency and throughput
- Optimize operational cost for production workloads
- Productionize machine learning model architectures
- Profile and identify GPU and CPU inference bottlenecks
- Scale inference systems for real users
Perks/Benefits
- Close collaboration
- Direct product impact
- Engineering focused culture
- High ownership
- Remote work flexibility
Skills/Tech-stack
Attention Mechanisms | Batching | CUDA | CUDA kernels | Compute Graph | Distributed Systems | KV cache | Language Models | Large Language Models | Neural Networks | ONNX Runtime | PyTorch | Python | Quantization | ROCm | Speculative decoding | Streaming | TensorRT | Triton | VLLM
Education
N/A
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