Machine Learning Engineer — Inference Optimization
Tasks
- Benchmark model performance across hardware and cloud
- Build and maintain inference serving infrastructure
- Collaborate with research to productionize model architectures
- Implement quantization and cache optimization techniques
- Improve monitoring reliability and observability
- Maintain ML infrastructure quality scalability and maintainability
- Optimize machine learning inference systems for latency and throughput
- Profile GPU and CPU inference bottlenecks
- Scale inference systems for production workloads
Perks/Benefits
- Collaborative teams
- Engineering focused culture
- Equity participation
- Ownership
- Product impact
- Remote work flexibility
Skills/Tech-stack
Attention Mechanisms | Batching | CUDA | Compute Graphs | Distributed Systems | GPU Performance | GPU performance tuning | Inference Optimization | KV cache | Language Models | Large Language Models | Low Latency | Low-Latency Services | Machine Learning | Memory Optimization | Network Architecture | Neural Network | Neural Network Architecture | Neural Networks | ONNX Runtime | Performance Tuning | PyTorch | Quantization | Speculative decoding | Streaming | TensorRT | Triton | VLLM
Education
N/A
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