Machine Learning Engineer — Inference Optimization
Tasks
- Benchmark model performance across hardware environments
- Build and maintain inference serving infrastructure
- Contribute to maintainable scalable ML infrastructure practices
- Implement quantization KV cache optimization speculative decoding batching streaming and model simplification
- Improve reliability monitoring observability and cost efficiency
- Optimize machine learning inference systems for latency throughput scalability and cost
- Productionize new model architectures with research teams
- Profile and identify GPU and CPU inference bottlenecks
Perks/Benefits
- Collaboration with research and product teams
- Engineering focused culture
- Equity participation
- Flexible remote work
- Opportunity to Work on Advanced AI Technologies
- Ownership
Skills/Tech-stack
Attention Mechanisms | Batching | CUDA | Compute Graphs | Distributed Systems | GPU Performance | GPU performance tuning | Hardware optimization | Inference Optimization | KV cache | Low Latency | Low-Latency Services | Machine Learning | Memory Optimization | Neural Networks | ONNX Runtime | Performance Tuning | PyTorch | Quantization | ROCm | Speculative decoding | Streaming | TensorRT | Triton | VLLM
Education
N/A
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