Sasha Harrison
Senior Machine Learning Engineer at Wayve
London, England, United Kingdom
Summary
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Senior🎓
Top SchoolSasha Harrison is a Senior Machine Learning Engineer based in London with nine years of experience building production ML systems and embodied intelligence for autonomous driving at Wayve and applied ML products at Scale AI. At Wayve she tech-led self-supervised pretraining and helped scale training pipelines by 10x, materially improving on-road performance and domain generalization for driving models. Her background blends applied math and CS from Stanford with hands-on roles from Google and Microsoft internships to productionizing NLP and vision pipelines at Scale, where she shipped high-throughput inference and edge-case mining features. An active contributor to Stanford’s CS131 computer vision coursework, she has implemented core linear algebra and image-manipulation utilities, reflecting a knack for bridging foundational research with production engineering.
9 years of coding experience
3 years of employment as a software developer
High School, High School at Pinewood SchoolBachelor’s Degree Mathematical and Computational Sciences, Bachelor’s Degree Mathematical and Computational Sciences at Stanford University
English, Spanish, Russian
Github Skills (8)
matrix-decomposition10
python10
image-manipulation10
linear-algebra10
eigenvalue10
numpy10
computer-vision8
jupyter-notebook7
Programming languages (2)
Github contributions (5)
StanfordVL/CS131_release
Sep 2019 - Nov 2019
Released assignments for the Stanford's CS131 course on Computer Vision.
Role in this project:
Data Scientist
Data ScientistContributions:19 commits, 19 PRs, 13 pushes in 1 month
Contributions summary:Sasha contributed to the course's homework assignments by adding and correcting content. The changes involved adding sanity checks and minor fixes to the provided IPython notebooks. Additionally, the user implemented linear algebra methods, including dot products, eigenvalue decomposition, and singular value decomposition within the `linalg.py` file. The user also worked on image manipulation tasks, such as image compression, color channel manipulation, and grey-scale conversion within the `imageManip.py` file.
stanfordpythonvisiondeep-learningcomputer-vision
sashahar/CS231N_race_and_facial_recognition
May 2019 - Jun 2019
Contributions:25 commits, 33 pushes, 1 branch in 29 days
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