Machine Learning Engineer — Inference Optimization
Tasks
- Benchmark model performance across hardware environments
- Build improve and maintain inference serving infrastructure
- Enhance quality scalability and maintainability of ML infrastructure
- Implement optimization techniques such as quantization KV cache optimization speculative decoding batching streaming and model simplification
- Improve reliability monitoring and observability for production workloads
- Optimize machine learning inference systems to improve latency throughput scalability operational cost
- Productionize new model architectures and translate research results
- Profile and identify bottlenecks in GPU and CPU inference pipelines
Perks/Benefits
- Direct product impact
- Engineering focused culture
- Equity participation
- Ownership over infrastructure
- Remote work flexibility
Skills/Tech-stack
Attention Mechanisms | Batching | CUDA | CUDA kernel | CUDA kernel tuning | Compute Graphs | Distributed Systems | GPU Performance | GPU Performance Optimization | Inference benchmarking | KV cache | Kernel tuning | Language Models | Large Language Models | Low Latency | Low-Latency Systems | Machine Learning | Memory Optimization | Model Serving | Neural Networks | ONNX Runtime | Performance optimization | PyTorch | Quantization | ROCm | Speculative decoding | Streaming | TensorRT | Triton | VLLM
Education
N/A
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