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Ron Estrin Ph.D.
Machine Learning CPU Compiler Engineer
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Location: Mountain View, California, United StatesApprox. Years of Experience: 10
Ron Estrin Ph.D.'s Current Workplace
Apple
Company Size
2500+
Amount Raised
$6.2B
We’re a diverse collective of thinkers and doers, continually reimagining what’s possible to help us all do what we love in new ways. And the same innovation that goes into our products also applies to our practices — strengthening our commitment to leave the world better than we found it. This is where your work can make a difference in people’s lives. Including your own. Apple is an equal opportunity employer that is committed to inclusion and diversity. Visit apple.com/careers to learn more.
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Notable Investors
Berkshire Hathaway, Sequoia Capital, Microsoft, Matrix Partners, Venrock
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Experience
Machine Learning CPU Compiler Engineer
Apple · Full-time
Jul 2023 - Present
1 yr 1 mo
Cerebras Systems
Sep 2019 - Jul 2023
Senior Member of Technical Staff
Nov 2020 - Jul 2023
2 yrs 9 mos
I lead a team of ~10 engineers to develop our automatic kernel generator and integrate it into the Cerebras Graph Compiler (CGC). Automatically-generated kernels are critical to the flexibility and robustness of our accelerator as they provide efficient on-demand kernels when hand-written implementations do not exist. I remain primarily hands-on and spend a large portion of my time implementing features and collaborating on technical problems within my team and across teams. Beyond the code generator, I have worked on and led several efforts to ensure the end-to-end robustness, reliability, and generalizability of the CGC, so that user's high-level model is efficiently compiled to binaries that execute the model at performance on the the wafer-scale engine.
Member of Technical Staff
Sep 2019 - Nov 2020
1 yr 3 mos
Software engineer designing and implementing an automatic code generator for the massively-parallel Cerebras Wafer Scale Engine. Using polyhedral compilation techniques, the code generator takes high level graph operations and compiles them to efficient low-level architecture-specific code for our chips.
PhD Research Intern
Google, LASER team
Jun 2017 - Sep 2017
4 mos
The primary goal of the internship was to research new approaches to computing low-rank matrix completions, such as the Hadamard Multifactorization, with a focus on applications like recommendation systems (e.g., movie/music recommendations) and for Natural Language Processing. I implemented high-performance solvers for computing approximate low-rank matrix factorizations using Weighted-Alternating-Least-Squares. The solvers were written in python using numpy and scipy, and continued to be used by the team for ongoing experiments after my internship ended. As part of the research, I demonstrated cases where Hadamard Multifactorization outperforms traditional low-rank matrix completion for computing word embeddings, particularly when computing embeddings for several languages simultaneously.
Science Researcher
The University of British Columbia
Jun 2016 - Sep 2016
4 mos
- Derived and developed fast iterative methods for (possibly non-symmetric) saddle-point linear systems; such linear systems are ubiquitous within engineering applications. - Work was performed in collaboration with Prof. Chen Greif.
Software Development Engineering Intern
Microsoft
Jun 2015 - Sep 2015
4 mos
- Worked in Elastic Scale team, implementing feature for distributed database transactions in the cloud using research conducted at Microsoft Research. - Created design document, implemented it within the SQL Server Engine code and implemented a test suite for the feature. - More details to follow when feature is in public preview.
Education
  • 2014 - 2019
    Stanford UniversityDoctor of Philosophy (Ph.D.), Computational and Mathematical Engineering
  • 2010 - 2014
    The University of British ColumbiaBachelor's Degree, Combined Honours in Mathematics and Computer Science