eggie5

Adyen | Founding Research Engineer, AI | ONSITE (San Francisco) | Full-time

Adyen is a global fintech platform powering payments, data, and financial services for companies like Meta, Uber, Spotify, and Nike. We’re profitable, publicly traded (AMS: ADYEN), and currently building a new AI research hub in San Francisco.

We’re hiring a Founding Research Engineer (AI) to help launch this team. You'll work directly with our SVP of Engineering and be the first AI research hire in SF, helping define our strategy and ship applied AI systems—focused on areas like foundational models, agentic workflows around identity and risk.

This is a hands-on role for someone with deep technical skill, strong product intuition, and a drive to build.

More info: https://careers.adyen.com/vacancies/6685352-founding-researc...

Questions? Reach out: [email protected]

Adyen | Sr DS & TL GenAI Team | Full-time | Madrid

Looking for SR DS and Team Lead roles for our tech-hub in Madrid. We have A100 GPUs and lots of use-cases were ready to execute on. Read about some of our recent work in agentic flows: https://www.adyen.com/knowledge-hub/data-agent-benchmark-for...

Looking for candidates with deep technical depth around language models.

Full relocation to Madrid.

Apply here: alex.egg[at]adyen.com and mention “HN - Who is hiring?” in the subject.

What's _online_ RL for an LLM? Saw this on the llama 3.3 reports too...

ah, so observe the reward and then take a gradient step

if you have an NN that is probabilistic, how do you update the prior after sampling from the posterior?

just applied for GenAI position in AMS. Can you transfer visas?

to add state (memory), u can either:

* inject the running chat log into the prompt * inject the summary of the chat into the prompt

LOL

I think the main criticism is portability of skills, ie everyone wishes ebay would just use react.

As Adams agreed, it was quite the perplexing philosophy. However, who won in the end? The firebrand Adams or stoic Jefferson???

everyone at ebay hates it -- the ebay blind is full of people hating on it

Years later, still puzzling over Thomas Jefferson's passivity at Philadelphia, John Adams would claim that "during the whole time I sat with him in Congress, I never heard him utter three sentence together"

Jefferson, himself would one day advise a grandson, "when I hear another express an opinion which is not mine, I say to myself, he has a right to his opinion, as I to mine." And "Why should I question it. His error does me no injury, and shall I become a Don Quixote, to bring all men by force of argument to one opinion?... Be a listener only, keep within yourself, and endeavor to establish with yourself the habit of silence, especially in politics."

yes and doesn't have traction inside ebay either :)

created at ebay and hated internally :)

in netherlands, for example, they (Goog) has to negotiate w/ the local-employee-backed Works Council. If they didn't bother setting one up, like Meta NL failed to, it will take about 3 months to do that. Then another 1-2 months for negotiations w/ the council. Then if they come to an agreement (they don't have to) w/ the redundancies and severance the affected can _then_ be notified.

for example, technically the meta layoffs back in November have not even happened here in NL and the affected won't even know until at least March-April!!

Interesting work on Online Learning:

There are many empirical studies which show for feature hashing, a few collisions don't have a sig impact on perf (https://youtu.be/ARjNMdCzN-Q?t=599).

However, for some archs, the impact is catastrophic. Eg matrix factorization. Any collision leads to an incorrect item. Zero Collision Hashing addresses the problem of mapping collisions. One technique is to introduce state into the hashing fn using the current id assignments.

real time publishing protocol:

* minute-level weight syncing * delta pushes only * ignore machine failures and rely on (possibly stale) full snapshot loading to bootstrap the new hosts

how much do you get paid and what city?

levels.fyi to get some data points.

do interviews

just saw an offer in Berlin for 150 base and 320k RSUs coming out to €230k annualized TC!!

Grubhub (grubhub.com) | ML Engineer | Full-time Search Data Science team is looking for a senior-level ML Engineer to help us drive search at Grubhub. Search team builds personalized recommender systems and other data products drive search-to-order conversion.

Some of our projects, to give you an idea of what you could work on, are:

* Counterfactual Evaluation: how would a change in our ranking algo affect conversion or revenue? * Counterfactual Learning: how can you use unbiased data from randomization to increase online metrics? * LTR: Making search more relevant and personalized * Bandits: how do you sample from the posterior of an arbitrary deep-learning model? * Online Learning: how can we learn incrementally instead of in batches? How do you update an embedding? * Multi-objective optimization. How can you balance conversion and revenue objectives? * Representation Learning: building a product graph from our catalog using large-scale language models * Semantic search: “chocolate milk” vs “milk chocolate” or not showing french fries and french toast of a “french” query

Perks: * Salary + RSUs * Our projects get lots of visibility/exposure as our treatments are the front page of the app. * We run many live experiments and have a freedom to try new ideas and influence the business from the bottom-up. * The team values research: we have a generous conference budget and active paper reading group. * Unlimited PTO * GPUs

Please reach out to me: [email protected]

You missed a big part: they did a big NAS run to make it work.

Do what do attribute to the gains? Adaptive clipping? Or $$$ spent on NAS??

Grubhub (grubhub.com) | ML Engineer | Full-time

Search Data Science team is looking for a senior-level ML Engineer to help us drive search at Grubhub. Search team builds personalized recommender systems and other data products drive search-to-order conversion.

Some of our projects, to give you an idea of what you could work on, are:

* Counterfactual Evaluation: how would a change in our ranking algo affect conversion or revenue?

* Counterfactual Learning: how can you use unbiased data from randomization to increase online metrics?

* LTR: Making search more relevant and personalized

* Bandits: how do you sample from the posterior of an arbitrary deep-learning model?

* Online Learning: how can we learn incrementally instead of in batches? How do you update an embedding?

* Multi-objective optimization. How can you balance conversion and revenue objectives?

* Representation Learning: building a product graph from our catalog using large-scale language models

* Semantic search: "chocolate milk" vs "milk chocolate" or not showing french fries and french toast of a "french" query

Perks:

* Salary + RSUs

* Our projects get lots of visibility/exposure as our treatments are the front page of the app.

* We run many live experiments and have a freedom to try new ideas and influence the business from the bottom-up.

* The team values research: we have a generous conference budget and active paper reading group.

* Unlimited PTO

* GPUs

Please reach out to me: [email protected]

reading gulag archipelago so i go that reference

anyone still like classic notebooks better???

why?

I've gotten the question in ML eng interviews 3 times: implement sparse vector and the respective dot product op. I always go for the handy DOK method.

roam

Managing uncertainty with Distributions instead of point estimates