as of september 2024, i’m a software engineer at anthropic! it’s really cool to be
part of growing so quickly with really smart people who genuinely care
about the safety and ethics of AI. i work on an internal tools team to
empower other employees to accelerate their workflow productivity and
move as fast as they would like to.
before that, i was a senior software engineer at iterative health who puts
technology to use for the benefit of gastroenterology. detecting colon
cancer earlier through computer vision and employing AI to speed up and
improve opportunities for patients.
and before that, i was a software engineer at amazon from 2014-2022 in:
order fulfillment backend systems — we made sure you got your order
when we promised by optimizing where the items come from, how they’re
transported, and so on.
ultra fast fulfillment warehouse management systems — improving and
expanding the UFF (prime now, amazon go, amazon fresh) software to
support new verticals.
accomplishments
professional things i’m proud of:
being a built
by girls mentor and helping bringing the gap that young women face
in tech
developing many developer productivity tools behind the scenes at
amazon such as a graphical interface for comparing a/b test results in
ci, a daemon that integrates with a browser extension that adds quick
links on internal sites to quickly launch shells to fleets, a script
that build a full intellij project from a service, which pulls the
autobuilt packages with the option of also pulling in the source for
other related dependencies and marks all the files correctly to allow
intellij to understand the project structure ‘out of the box’
implementing features in amazon warehouse management systems: 1)
work assignment tweak that allowed better social distancing during the
covid pandemic, 2) a distance-based pick planning heuristic that reduced
the amount of walking warehouses workers needed to do by 1 mile per
day
making a breakthrough in the investigation of a memory management
issue in fulfillment planning that allowed us to reduce our expected
fleet scaling by ~30% before peak season
developing a user-friendly deployment script that connects multiple
data sources such as ECS, git, kube to show a preview of what is already
deployed, what is going to be deployed, version differences, and links
to wikis that should be updated after deployment. it builds things in
parallel, has progress bars, names things according to our internal
conventions, checks all prerequisites before proceeding and gives
warnings if anything looks out of the ordinary and all around just makes
it hard to do the wrong thing.
lead the charge on using llms to pull out and present key
information about patients from their health records that allow
specialists to give more time to screen patients for clinical drug trial
eligibility, raising the number of potential patients we can help
connect to effective treatment
my linkedin profile:
elle-trudgett