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Machine Learning Engineer specializing in perception systems for autonomous vehicles at Cruise. Based in San Francisco.
Basics
| Name | Jeremy Malloch |
| Label | Machine Learning Engineer |
| jeremywebsite@malloch.ca | |
| Url | https://jeremalloch.github.io/ |
| Summary | Machine Learning Engineer specializing in perception systems for autonomous vehicles |
Work
-
2021.07 - Present Machine Learning Engineer
Cruise
Working on perception systems for autonomous vehicles
- Unblocked Cruise's return to driverless operation by leading Unified Tracker V2 model release that resolved critical ultra-nearfield risks (largest perception risk area)
- Contributed multiple modelling improvements to novel Unified Tracker V1 multi-modal, temporal tracking transformer model
- Increased model flop utilization by 32%, stabilized training by analyzing model activation magnitudes, and reduced velocity flips by 20%
- Created model attention metrics leading to 11% reduction in centroid error, 12% reduction in velocity error, and increased recall of pedestrians from 95.03% to 98.33%
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2019.01 - 2019.08 Autonomous Systems Software Intern
Apple
Building on device ML inference infra for autonomous vehicles
- Contributed to machine learning compiler and inference framework, using C++ and Python
- Researched & prototyped DNN quantization & sparsification pipeline, resulting in up to 40% faster runtime of deployed models
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2018.05 - 2018.08 Machine Learning Intern
Cognitive Systems
Developed ML models & inference tooling for WiFi based motion detectors
- Achieved 60x improvement in neural network inference runtime by implementing im2col convolutions, vectorization and memory alignment with Eigen in C++ for ARM CPU (exceeded Tensorflow Lite baseline by 2x)
- Researched & prototyped semi-supervised GAN data augmentation to reduce false positives
Education
-
BMath
University of Waterloo
Combinatorics & Optimization and Computer Science (Double Major)
- Relevant Coursework: Advanced Algorithms, Algorithms, Operating Systems, Computer Vision, Mathematical Optimization (Convex & Linear Programming), Computational Discrete Optimization, Deep Learning in Computational Discrete Optimization (graduate level), Optimization for Machine Learning (graduate level)
Skills
| Programming Languages | |
| Python | |
| C++ | |
| CUDA (introductory) |
| ML Frameworks | |
| PyTorch | |
| JAX | |
| Keras |
Interests
| Hobbies | |
| Hiking | |
| Cycling | |
| Photography | |
| Open Source Contribution | |
| Reading |