Machine Learning Engineer — Inference Optimization
Tasks
- Benchmark model performance across hardware environments
- Build and maintain inference serving infrastructure
- Deploy and productionize machine learning model architectures
- Implement quantization and KV cache optimization
- Improve reliability monitoring observability and cost efficiency
- Optimize machine learning inference systems for latency and throughput
- Profile and identify bottlenecks in GPU and CPU inference pipelines
Perks/Benefits
- Close collaboration with research infrastructure and product teams
- Engineering focused culture
- Equity participation
- Flexible remote work
- Opportunity to work on advanced AI systems
- Ownership and autonomy
Skills/Tech-stack
Attention Mechanisms | Batching | CUDA | Compute Graphs | Distributed Systems | GPU Performance | GPU Performance Optimization | Hardware acceleration | Inference Optimization | KV cache | Low Latency | Low-Latency Systems | Machine Learning | Memory Optimization | Neural Networks | ONNX Runtime | Performance optimization | PyTorch | Quantization | ROCm | Speculative decoding | Streaming | TensorRT | Triton | VLLM
Education
N/A
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