Prolific | Senior Software Engineer | Hybrid ONSIDE 1-2 days/wk Bay Area | $200k-$250k
Prolific is not just another player in the AI space – we are the architects of the human data infrastructure that's reshaping the landscape of AI development. In a world where foundational AI technologies are increasingly commoditized, it's the quality and diversity of human-generated data that truly differentiates products and models.
We’re looking for impact-focused Software Generalists to join our specialized team focused on serving frontier model creators and enterprise AI application developers. As a full-stack engineer, you will work across Prolific’s domains to solve customer and product problems.
This is an exciting opportunity to work directly with frontier AI companies, making critical technical decisions that balance scrappy startup execution with scalable, reliable engineering, as Prolific revolutionizes research for the AI community. You'll will have regular in person collaboration with customers and our US team, as well as collaborate closely with our UK-based tech teams.
Unfortunately we don't sponsor US visas at this time.
context-dependent, or "reified" assertions are a pain point for sure. I come from the perspective of cultural heritage data, where context is king. Which expert made this attribution for this painting? Who owned it _when_? According to which archival document? etc.
Almost all the engineering problems cited in the original post are still basically there, but graphical models are still the least painful way of doing this, particularly when trying to share data between institutions. Example: https://linked.art/model/assertion/
I find that a fascinating reaction given how rapidly %>% have been taken up across a large segment of the R universe, to great excitement! Personally, I find it far MORE legible than endlessly-nested function calls.
It results in code that more closely resembles executed order of operations (e.g. filter -> mutate -> group -> summarize). Context is also key: it's most often used for data processing pipelines in specific analytical scripts or literate-code documents - less so used when defining generalizable/testable functions in packages (again, just a personal perspective - YMMV of course)
A related aside: while forgeries - deliberate imitations to mislead and deceive - are exciting, they only represent a very tiny portion of art attribution questions. In reality, these tend to deal more with discerning between artists working in the same period, rather than those attempting to fool the eye at several centuries' remove.
For example, the Rembrandt Research Project infamously set out to identify genuine vs. fake Rembrandt paintings in his corpus of known works under the false assumption that there would be a lot of 18th/19th/20th century forgeries. In fact, most of the "non-Rembrandt" cases they found were not later imitations, but instead works done by his own students or contemporaries - or works co-produced by Rembrandt and another. The result - deconstructing the project's original false assumption - proved revolutionary for our understanding of artistic studio practice from the period, but failed to locate many "forgeries" as such.
I'm working on pulling the images now, like I did for the Rijksmuseum CC0 dump. FWIW a good place to host that torrent is the Internet Archive - it's great for discoverability.
Prolific is not just another player in the AI space – we are the architects of the human data infrastructure that's reshaping the landscape of AI development. In a world where foundational AI technologies are increasingly commoditized, it's the quality and diversity of human-generated data that truly differentiates products and models.
We’re looking for impact-focused Software Generalists to join our specialized team focused on serving frontier model creators and enterprise AI application developers. As a full-stack engineer, you will work across Prolific’s domains to solve customer and product problems.
This is an exciting opportunity to work directly with frontier AI companies, making critical technical decisions that balance scrappy startup execution with scalable, reliable engineering, as Prolific revolutionizes research for the AI community. You'll will have regular in person collaboration with customers and our US team, as well as collaborate closely with our UK-based tech teams.
Unfortunately we don't sponsor US visas at this time.
Apply: https://job-boards.eu.greenhouse.io/prolific/jobs/4767348101