Senior ML Engineer
Tasks
- Collaborate with research engine tooling and QA teams
- Conduct model pruning and weight clustering
- Curate large scale datasets and generate synthetic data
- Define best practices for performance and SDK delivery
- Define model inputs and outputs and preprocessing postprocessing
- Design training curricula and augmentation strategies
- Distill and compress machine learning models
- Implement model CI CD and model registries
- Implement quantization aware training and quantization
- Integrate ML inference into production rendering pipelines
- Mentor engineers and conduct code reviews
- Optimize neural network architectures for efficient inference
- Perform inference optimization for production
- Profile latency throughput and memory footprint
- Track experiments and ensure model reproducibility
Perks/Benefits
Skills/Tech-stack
C++ | CI/CD | CUDA | CUDNN | Containerization | Distributed Training | EfficientNet | Experiment tracking | Instant NGP | JAX | Knowledge Distillation | Low Rank SVD | MLOps | Mixed Precision | MobileNet | Model Registry | Nerf | Neural Rendering | ONNX | ONNX Runtime | Operator fusion | Pruning | PyTorch | Python | Quantization | Quantization aware training | Sparsity | Telemetry | TensorFlow | TensorRT | Testing | UNet | Version control | Vision Transformer | Weight Clustering
Education
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