Alexander Saavedra
Email is available upon request only via LinkedIn | San Francisco, CA
Summary
Alexander is a current Sr Staff Solutions Architect working in application security with a strong cybersecurity, machine learning, and software development background. His current role he has been ranked number 1 in his division two consecutive years and won the division West SE of the year award 2022 at Synopsys which is a 20,000+ person company. Alexander has a relentless focus on delivering value to customers with an innovators mindset in all of his work. He focuses on making every day “day one” and making things 10 times better by rethinking what is considered possible. While in College he has worked as a Machine Learning research intern, Machine Learning engineer intern, and engineer intern achieving a UC Major GPA of 3.72 in Machine Learning and Neural Computation. He is currently pursuing a Master’s in Computer Science, 4.0 GPA in Interactive Intelligence (like Machine Learning) at Georgia Institute of Technology which is a top 10 Computer Science program.
Hobbies: Reading, Biking, Weightlifting, Computer Games, Economics and Markets, Writing, Longevity and Health, Pedagogy, exploring future technology trends, poetry
Technical & Analytical Skills
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General: Java and Kotlin ecosystem, TypeScript and Javascript, NodeJS, SQL, Postgres, Maven, Gradle, Git, basic Networking, REST APIs and exposure to SOAP, Bash, Linux and general GNU knowledge, LaTeX, Vim, and relational & non-relational databases
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Deployment Technologies: Amazon Web Services, Docker, Kubernetes, KiND, GitHub Actions, Azure DevOps, Jenkins, Virtual Machine Infrastructure, Ephemeral Agent Deployment, Firebase
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Development Methodologies: Extreme programming, Test Driven Development, Behavior Driven Development, Agile, and Scrum
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Application Security Testing: Static Application Security Testing, Software Composition Analysis, Interactive Application Security Testing, Infrastructure as Code, Security in Software Development Lifecycle, and basic Protocol Fuzzing knowledge
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Machine Learning: Python 2 & 3, PyTorch, TensorFlow, advanced Statistics, Linear Algebra, high dimensional Math, and Multivariable Calculus
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Cryptocurrency: Solidity, Remix, Ethereum Virtual Machine, Smart Contract Development, and functional encryption knowledge
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Prediction Markets and Forecasting: Ask me, but things like Metaculus.com tutorials and goodjudgment.com may give you some idea what this is. In a fast moving field like software you need to forecast the future with some degree of accuracy, not just precision, to stay relevant
Work Experience
Sr. Staff Solutions Architect | Synopsys Inc | San Francisco Bay Area | 2021 - present
- Spearheaded product security initiatives for machine learning both internally results in more ubiquitous ML usage
- Lead LLM team initiative meetings to incorporate LLM tooling to more efficiently answer internal product queries
- Discussed and helped weigh large language model options internally for incorporation
- Streamlined, and documented Kubernetes deployment strategies in concert with specialist team on K8 deploy
- Spearheaded the initiative for high priority technology integrations like GitHub Actions for SAST and SCA
- Mentored, and assisted in onboarding 5 senior and 2 staff engineers leading to 150% faster onboarding & product
- Streamlined the product trial navigation process with engineer teams leading to a 200% faster proof of concept cycle
- Trained engineers on infrastructure as code and modern SDLC best practices resulting in further POC alignment
- Achieved highest division quota attainment of 163% in first year
- Awarded SE of the Year 2022 for my division region
Solutions Engineer | Launchdarkly | San Francisco Bay Area | 2019 - 2020
- Closed 6 figures of product for the fiscal quarter during the pandemic
- Worked with the top account executive of the year leading to heavy technical involvement in large enterprise deals
- Produced content for marketing, learning department, security, and on site templates
Software Engineer | Bina Technologies acquired by Roche | San Francisco Bay Area | 2016 - 2019
- Designed user acceptance tests based on FDA guidelines, implementing Cucumber and Protractor test suites for FDA tests of cancer detection products, achieving over 60% increase in UI test coverage
- Created an onboarding program for engineers, which reduced overall ramp time across 10 new hires by 30%
- Automated Jenkins jobs using Groovy to run verification suites with smoke tests and long-running feature tests for builds, optimizing 8 hours of manual tests down to 45 minutes of automated tests 10X decrease
- Implemented API testing and reporting framework in RestAssured, JUnit, TestNG used by development and QE for testing endpoints, leading to a 36% increase in bug discovery on endpoints
- Worked with the lead genomic pipeline engineer and backend engineer to create an analysis pipeline engine database for categorizing cancer types in blood and tissue using relevant and up to date cancer sequence snippets in our sequencing runs
- Leveraged SonarQube for guidance in reducing code smells, security vulnerabilities, and code complexity by 92%.
Software Engineer | Redbird Advanced Learning | San Francisco Bay Area | 2014 - 2015
- Initiated and executed automated regression testing suites written in Mocha, PhantomJS, BackboneJS, and Ruby Watir increasing bugs discovered in the course curriculum by 200%
- Led 3 person team responsible for finding and resolving issues in Math and Language Arts Product, leading to a 50% increase in bugs discovered within the curriculum and resolved
Machine Learning Engineer Intern | Qualcomm Institute | San Diego, CA | 2013 - 2014
- Implemented machine learning methods for transforming satellite image data with Python, SciPy, NumPy, and Django Database, leading to path discovered in Valley of the Khans in Mongolia for National Geographic
- A full description of the research to attempt to discover Genghis Khan’s tomb by my manager Andrew Hyunh can be found here
Machine Learning Research Intern | Redacted Company | San Diego, CA | 2012 - 2013
- Project under NDA, I worked as a machine learning researcher for a private company along with help from Professor Virginia de Sa a world renowned Machine Learning, Brain Computer Interface, and Computational Neuroscience researcher
Projects
Graduate Research on AI Assistants for Productivity and Creativity Enhancement
- AI assistants such as large language models increase many information technologies productivity but also reduce unwanted side effects like keeping them shallow in their understanding or unthinking when using solutions which ultimately reduces the personal autonomy and creativity of the individual.
- This research seeks to mitigate these unwanted side effects, boost productivity and other desired effects, and ideally find fertile research to enhance these while still using AI assistant tools using mixed methods both qualitative and quantitative to investigate this topic.
- This is especially the case for experts who have seen the least gains from these technologies thus far, the results from this research demonstrate large gains for experts which have not previously been found in the body of research on this topic both in creativity and speed of results without reducing precision.
- Abstract accepted to a prestigious journal’s special issues on machine learning and awaiting further review
Guru Indexer, Guru investor returns for index fund costs
- Guru index which aggregated stock data and metadata from various sources which was I believed would produce a highly concentrated long term outperformance focused strategy for large and small capital US based companies sources included GuruFocus and WhaleWisdom. The rough details are outlined in this blog post https://awsaavedra.com/posts/enterprising-investor.
- Strategy was eventually discussed with highly skilled value investors and then discontinued once I learned about a YC18 alumni startup that was implementing a strategy that rhymed in their flagship product with more traction.
GarageHub, the Airbnb of garage space
- Developed ‘GarageHub’ – AirBnB for garage space – utilizing JavaScript, AngularJS, Firebase, Zapier, and Twilio API, designed to capture a $20Mill+ market of open garage space in San Francisco
Education
M.S. Computer Science: Interactive Intelligence GPA: 4.0, Georgia Institute of Technology, 2023 - Present
B.S. Machine Learning and Neural Computation UC MGPA: 3.72, Minor in Computer Science, University of California San Diego
CoFounder and Principal Member, Hackathons at UC San Diego 2013 - 2014
Certifications and Awards
- Honors Scholar Transfer Award 2011
- Blockchain Basics
- Blockchain Platforms
- Decentralized Finance(DeFi): The Future of Finance
- Smart Contracts
- Convolutional Neural Networks
- Neural Networks and Deep Learning
- Structuring Machine Learning Projects
- Improving Deep Neural Networks: Hyperparameter Tuning, Regularization, and Optimization
- Learning How to Learn by Barbara Oakley
- AWS Certified Solutions Architect - In progress