Hearst | Senior Engineer, Generative AI | New York, NYC | Hybrid/Remote
Here at Hearst we've been building out generative AI use cases and fluency across our portfolio of businesses spanning well known media brands to healthcare, transportation, and financial services. While we are an established corporation with multi-national presence, the mission and support behind our generative AI team more closely resembles a well-funded early stage startup as we engage in radical business transformation empowered by nascent technology.
The ideal candidate is one who has written code but now builds systems largely leveraging agentic development tools such as Claude Code (w/ Opus 4.5) & Codex (w/ GPT-5.2), and who sees the future of development as managing swarms of AI agents rather than cranking out code by hand.
Come join our team and help flesh out the future of business on a global sale while getting deep hands-on experience with the latest and greatest tech!
Hearst | New York City - Hybrid or Remote | Full-time | Senior Engineer, Generative AI & Senior Program Manager, Generative AI
Hearst's corporate team is looking to fill two roles (in Engineering & Program Management) on its Generative AI Team, working to empower businesses across our diversified portfolio spanning publishing, health, transportation, and financial services. Our team's purview includes evaluating and benchmarking new releases, experimenting with the best ways to apply them to existing business problems, piloting POCs that demonstrate new ways of doing things, and accelerating the entire process on our team and within our various businesses through AI driven development tools & tactics.
Do you enjoy actively learning and experimenting with new tools & processes? Do you like solving problems across a wide range of domains and disciplines? Are you excited by the potential and application of emergent generative capabilities and agentic autonomous systems? Check out one of our open postings or feel free to reach out to me directly (https://www.linkedin.com/in/alexandria-redmon-5078865/):
I envy your rig - mine glitched a lot to get it in <3min. Might not be doing myself a service by actually answering the Duolingo questions via LLM... https://www.youtube.com/watch?v=I-J0ppP-H9s
Wanted to see how quickly I could get to the beach - was limited by my system resources and an inability to load the closing video. Auto-solving the Duolingo questions was clearly the best part.
A progress meter may not be necessary, but as the article points out if you have comments or an otherwise large footer not associated with the content of the post, the scrollbar can be deceiving.
I'm personally a huge fan of the progress meter (having once thought it was redundant as well) - one other easy addition I didn't see mentioned is an "estimated reading time." Having a ballpark range for how long I should expect to spend with a piece of content greatly increases my chance of engaging with it, and the progress meter creates a tangible representation of that time (and how much of it I have left to finish consuming the content).
I'm not sure how "estimated reading time" works for non-english speakers.
As for "progress meter" being an optimization of websites with large footers or long lists of comments, I'd argue that a better optimization technique would be to remove the footer, or remove/hide by default list of comments (i.e. make it available with a mouse click somewhere).
> I'm not sure how "estimated reading time" works for non-english speakers.
>
Do you mean for non-native speakers reading English material or for material in other languages when read by native speakers with an average reading competence in their native language?
Anyhow, as a non-native English speaker, it implemented it on my site by showing both the word count, as well as an estimated time, which is simply calculated by dividing the word count by a constant number of assumed words per minute.
That number was guesstimated by doing some reading speed tests on myself and researching average reading speeds and rounding up to 10 wpm. I have to admit that my assumption of 240 words per minute does not hold up to any scientific scrutiny, but otoh it is an estimation.
It works ok for prose. As soon as other notation is mixed in (code, math, graphs, diagrams etc.pp) that begins to naturally to vary wildly in accuracy.
>I'm not sure how "estimated reading time" works for non-english speakers.
Do you mean people whose native language is not English? I assume it works the same way as for everyone else, i.e. it's always an approximation anyway. I'm not from an English-speaking country, but I'm pretty sure I read faster than the average English native.
The other interpretation is that you mean people who can't read English at all, but in this case I understand even less. They won't be able to read the article, if they don't speak the language. After automated translation, the reading time should be roughly correct again.
in the early days of blogs, i found comments to be interesting and engaging dialogs. the last decade, or so, i’ve found comments to be unhelpful nitpicking/personal disagreements, spam, or support requests, so i’ve completely stopped concerning myself with comments on blogs.
so i’ll agree that progress indicators are helpful for this use case.
This is awesome! Until GPT-4o dropped, Claude 3 Opus was hands down my go-to for code generation.
Between these model performance improvements and their new "artifacts" handling, I get the impression this update may sway me strongly back towards Anthropic (at least for this use case).
Anyone here used this yet? I've been a big fan of their Gen-2 offering so excited to get my hands on longer generations with higher quality and consistency
Started working on something to build complete multi-file changes via LLM and chose to write it as a CLI tool both for convenience and ability to compose feature builds via scripts and other tools.
It supports various providers (OpenAI, Anthropic, Azure, Gemini), supplying both local and remote resources (defined by arguments and/or configuration file directives), and allows for feedback after initial implementation which is useful for troubleshooting issues in initial tack.
A few things I plan on adding soon include:
- Improved source mapping
- Hands-free voice mode
- Image processing
Would love to see Ollama support for this - seems promising given my experience with LLaVA so far and would love to get some hands on head to head experience
Here at Hearst we've been building out generative AI use cases and fluency across our portfolio of businesses spanning well known media brands to healthcare, transportation, and financial services. While we are an established corporation with multi-national presence, the mission and support behind our generative AI team more closely resembles a well-funded early stage startup as we engage in radical business transformation empowered by nascent technology.
The ideal candidate is one who has written code but now builds systems largely leveraging agentic development tools such as Claude Code (w/ Opus 4.5) & Codex (w/ GPT-5.2), and who sees the future of development as managing swarms of AI agents rather than cranking out code by hand.
Come join our team and help flesh out the future of business on a global sale while getting deep hands-on experience with the latest and greatest tech!
Apply via https://eevd.fa.us6.oraclecloud.com/hcmUI/CandidateExperienc... or feel free to reach out to me directly at alex dot redmon at hearst dot com.