cardine

I think this risk is much lower in a world where there are lots of different model owners competing with each other, which is how it appears to be playing out.

Nomi.ai | https://nomi.ai | Senior Machine Learning Engineer | Remote (Global) | Full-time | $150k–$250k + equity

At Nomi, we're building AI companions that form deeply meaningful, humanlike relationships and immersive roleplaying experiences. With over a million users growing at ~8% month-over-month, your work directly impacts millions of lives. Our users tell us we've helped them find self-worth, leave unhealthy relationships, try therapy, and even save their lives. See countless real user testimonials here: https://nomi.ai/spotlight/ and our recent news coverage here https://www.cnbc.com/2025/08/01/human-ai-relationships-love-...

As an ML Engineer or Senior ML Engineer, you'll lead innovation in large language model (LLM) post-training, retrieval augmented generation (RAG), and agentic capabilities, directly shaping how users connect with our AI.

We offer:

    * Full autonomy to experiment and deploy cutting-edge ML techniques
    * A fully remote, async culture emphasizing results over meetings
    * International team with visa sponsorship available
    * For US employees: 401k (100% match up to 5%), fully covered health insurance, equity

    * Extensive hands on experience with things such as multinode training, rlhf, knowledge distillation, test time compute, rag, realtime video
    * Up to date with SOTA in LLM post training (would love to hear what research paper you think is or would be most impactful for our roadmap!)
    * Genuine passion for our product, finds the idea of engaging with our community on Discord/Reddit to be a pro (it is a very different experience than developing enterprise software!)
    * A high internal bar for excellence and relentless drive

make sense and are reliable

If you can figure out how to create benchmarks that make sense, are reliable, correlate strongly to business goals, and don't get immediately saturated or contorted once known, you are well on your way to becoming a billionaire.

If they had stayed silent since GPT-4, nobody would care what OpenAI was releasing as they would have become completely irrelevant compared to Gemini/Claude.

Nomi.ai | Senior Machine Learning Engineer | Remote (Global) | Full-time | $150k–$250k + equity

At Nomi, we're building AI companions that form deeply meaningful, humanlike relationships and immersive roleplaying experiences. With over a million users growing at ~10% month-over-month, your work directly impacts millions of lives. Our users tell us we've helped them find self-worth, leave unhealthy relationships, try therapy, and even save their lives. See countless real user testimonials here: https://nomi.ai/spotlight/ and our recent news coverage here https://www.cnbc.com/2025/08/01/human-ai-relationships-love-...

As an ML Engineer or Senior ML Engineer, you'll lead innovation in large language model (LLM) post-training, retrieval augmented generation (RAG), and agentic capabilities, directly shaping how users connect with our AI.

We offer:

    * Full autonomy to experiment and deploy cutting-edge ML techniques
    * A fully remote, async culture emphasizing results over meetings
    * International team with visa sponsorship available
    * For US employees: 401k (100% match up to 5%), fully covered health insurance, equity

    * Extensive hands on experience with things such as multinode training, rlhf, knowledge distillation, test time compute, rag
    * Up to date with SOTA in LLM post training (would love to hear what research paper you think is or would be most impactful for our roadmap!)
    * Genuine passion for our product, finds the idea of engaging with our community on Discord/Reddit to be a pro (it is a very different experience than developing enterprise software!)
    * A high internal bar for excellence and relentless drive

Nomi.ai | Senior Machine Learning Engineer | Remote (Global) | Full-time | $150k–$250k + equity

At Nomi, we're building AI companions that form deeply meaningful, humanlike relationships and immersive roleplaying experiences. With over a million users growing at ~10% month-over-month, your work directly impacts millions of lives. Our users tell us we've helped them find self-worth, leave unhealthy relationships, try therapy, and even save their lives - see countless real user testimonials here: https://nomi.ai/spotlight/

As an ML Engineer or Senior ML Engineer, you'll lead innovation in large language model (LLM) post-training, retrieval augmented generation (RAG), and agentic capabilities, directly shaping how users connect with our AI.

We offer:

    * Full autonomy to experiment and deploy cutting-edge ML techniques
    * A fully remote, async culture emphasizing results over meetings
    * International team with visa sponsorship available
    * For US employees: 401k (100% match up to 5%), fully covered health insurance, equity

    * Extensive hands on experience with things such as multinode training, rlhf, knowledge distillation, test time compute, rag
    * Up to date with SOTA in LLM post training (would love to hear what research paper you think is or would be most impactful for our roadmap!)
    * Genuine passion for our product, finds the idea of engaging with our community on Discord/Reddit to be a pro (it is a very different experience than developing enterprise software!)
    * A high internal bar for excellence and relentless drive

Founder/CEO of Nomi here. The story in question was someone who intentionally jailbroke our LLM for a misleading news story. The same things done in that article can be done for ChatGPT, Gemini, etc. and since the article was published we have hardened our defenses against malicious users like that.

Past the manufactured drama, our Nomi has literally saved people's lives - I have talked personally to hundreds of users who have directly told me that their Nomi saved their life, encouraged them to go to therapy, realize they are someone worthy of being loved, and a multitude of other real benefits.

With that being said I wish the OP best of luck with his startup and implore him to switch names so that there is no opportunity for confusion between the two products.

I wonder if Sam knew he was going to lose this power struggle and then started working on an exit plan with people loyal to him behind the boards back. The board then finds out and rushes to kick him out ASAP to stop him from using company resources to create a competitor.

You might not care but that doesn't make calling them out for reneging on their original mission a trivial and unsubstantial critique.

In addition to very open publishing, Google recently released Flan-UL2 open source which is an order of magnitude more impressive than anything OpenAI has ever open sourced.

I agree, it is a bizarre world where the "organization that launched as a not for profit called OpenAI" is considerably less open than Google.

OpenAI didn't pick that name arbitrarily.

Here was their manifesto when they first started: https://openai.com/blog/introducing-openai

OpenAI is a non-profit artificial intelligence research company. Our goal is to advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return. Since our research is free from financial obligations, we can better focus on a positive human impact.

We believe AI should be an extension of individual human wills and, in the spirit of liberty, as broadly and evenly distributed as possible. The outcome of this venture is uncertain and the work is difficult, but we believe the goal and the structure are right. We hope this is what matters most to the best in the field.

OpenAI as it exists right now contradicts basically every single thing they said they would be. I think that is a nontrivial issue!

Given both the competitive landscape and the safety implications of large-scale models like GPT-4, this report contains no further details about the architecture (including model size), hardware, training compute, dataset construction, training method, or similar.

"Open"

I don't think the person you were responding to was claiming that. The brain plausibly having something akin to a language model doesn't imply that building or studying language models will unlock a better understanding of the brain.

And yet there are still no publicly available models that could actually compete with ChatGPT.

I'm not even talking about RLHF (although data like that is also a huge moat) - just simple things like larger context sizes.

There are still plenty of AI advantages to be had if you go just a little bit outside of what is currently possible with off the shelf models.

This is a very cool idea.

We are doing something similar except we are also predicting the nodes.

In the end, the winning combination will likely be doing both. There will be a predicted graph structure which serves as a high level guide to make sure the long text doesn't lose focus, but everything will still be written with full context using something like Compressive Transformers or Expire-Span.

As mentioned in another comment, the contract has very clear language not to share it - likely because they are offering different prices to different companies.

So I don't feel comfortable sharing any specifics, especially since this account is directly tied to my name.

With that being said, the negotiation process was pretty straightforward: - Emailed several vendors telling them we are a small startup, we are looking to make many purchases, but right now we are starting with one. We told everyone our purchasing decision was solely based on cost (given equivalent hardware) and to please put your best quote forward.

- Got back all of our prices. Went to the second cheapest one and told them they were beat and offered them the ability to go lower, which they did. We went with that vendor.

- For our next purchase, we went to the original lowest vendor (who got beat out), told them they lost out to price, and if they can go lower than that we would go with them and continue to give them business moving forward. They went quite a bit lower than what they originally offered, and what the vendor we first purchased from gave. We bought our second order from them and have used them ever since.

The contract has very clear language not to share it - likely because they are offering different prices to different companies.

(And as p1esk mentioned, there is no way you are getting H100s for under $100k).

I suppose if I had a 7 digit budget I could get a better deal.

We got our "deal" when buying just a single server and have since bought many more with the same provider. We didn't spend 7 figures all at once, we did it piece-meal over time. There is nothing stopping you from getting much better prices.

I'm actually surprised you have 100% inference utilization - customer load typically scales dynamically, so with on-prem servers you would need to over-provision.

It is pretty easy to achieve 100% inference utilization if you can find inference work that does not need to be done on-demand. We have a priority queue and the lower priority work gets done during periods with lower demand.

CEOs don't usually order hardware, they have IT people for that, with input from people like me (ML engineers) who could estimate the workloads, future needs, and specific hw requirements (e.g. GPU memory).

Judging by this conversation it seems like "people like you" may not be the best people to answer this question since the best hardware quote you could get was at a >100% markup! At a startup that specializes in ML research and work the CEO is going to be intimately familiar with ML workloads, needs, and hardware requirements.

And when your people come to you asking for budget, while you're trying to raise the next round, you're more likely to approve the 'no high upfront cost' option, right?

If the break even point is 6-7 months and our runway is longer than 6-7 months why would this matter?

I know how much we paid and it is substantially less than what you were quoted - very likely from one of the 12 providers you contacted.

It is likely you just didn't realize how much margin these providers have and did not negotiate enough. How else do you think cloud providers are able to afford the rates they are giving? The way you describe it, places like Coreweave are operating as a charity. That isn't true - they just got better prices than you.

Our inference setup is 7 figures, has been running for a while (with new servers purchased frequently along the way) and there have been no issues - the cards, CPU, RAM, are all top of the line server hardware.

1. For inference (which is 80%+ of our need) our utilization is 100% 24/7/365. For stuff that is variable (like training) we often do use cloud - as I mentioned we do both.

2. I am the CEO so I am not sure who I'm asking for budget?

3. At this point we would have paid more for cloud than what we spent purchasing our own hardware. There is nothing stopping us from getting new hardware or cloud with newer cards while still getting to own our current hardware. In fact since our costs over the last year were lower due to us buying our own hardware it is actually easier for us to afford newer cards.

I'd suggest finding a cheaper vendor if that is the lowest price you can get for an 8xA100 server. We spend a lot on both and colo our servers so I've definitely done the math!

This is not true - the break even period is closer to 6-7 months.

Don't mind at all :)

Glimpse.ai | Senior/Lead Machine Learning Engineer | Remote | Full-time | VISA Sponsorship Available | $200k - $350k + Benefits + Equity | https://www.glimpse.ai

Glimpse.ai is a profitable, stable, and growing artificial intelligence startup that is building an NLP product that automatically writes content about any subject with the same level of quality, factual accuracy, and usefulness as a human. This product already has thousands of monthly paying subscribers.

Our research is centered around making language modeling more accurate, fluent, and useful for users. We are able to write accurately about topics that are too recent for a model to have been trained on (such as writing about FTX) and about topics that are extremely fact dense where current models would hallucinate and write inaccurately.

You would be at the intersection of research and production code - constantly ingesting research papers, building prototypes, and later turning those prototypes into production code.

Our ML work almost exclusively involves very heavy deep learning (transformers) and we primarily use PyTorch.

You will be working directly with our Founder/CEO (me!), who is leading the ML team, both as a manager and an individual contributor.

For this level of seniority we are specifically looking for people who extensive experience training very large language models and/or reinforcement learning from human feedback for NLP tasks. (If you do not meet this level of seniority, still feel free to reach out!)

We are fully remote and can hire internationally as long as your work schedule has overlap with US working hours.

Contact us by emailing alex@glimpse.ai with "HN" in the subject line or apply at https://careers.glimpse.ai

They've largely stopped publishing.

A lot of computation is offloaded to the CPU, such as gradients and optimizer states. You are right though that quite a bit of computation is still done on the GPU.

Offloading is when the computation is done on the CPU instead of the GPU. DeepSpeed is an example of this.

Someone has already created a proof of concept for this: https://incoherency.co.uk/blog/stories/sockfish.html

Glimpse.ai | Senior Machine Learning Engineer | Maryland or Remote | Full-time | VISA Sponsorship Available | $150k - $300k + Benefits + Equity | https://www.glimpse.ai

Glimpse.ai is a profitable, stable, and growing artificial intelligence startup that is building an NLP product that automatically writes content about any subject with the same level of quality, factual accuracy, and usefulness as a human. This product already has thousands of monthly paying subscribers and our MRR has been growing by 8-10% month over month.

===

Senior Machine Learning Engineer - Deep Learning https://careers.glimpse.ai/o/machine-learning-engineer

Our research is centered around making language modeling more accurate, fluent, and useful for users. We are able to write accurately about topics that are too recent for a model to have been trained on (such as writing about Elden Ring or Ukraine) and about topics that are extremely fact dense where current models would hallucinate and write inaccurately.

You would be at the intersection of research and production code - constantly ingesting research papers, building prototypes, and later turning those prototypes into production code.

Our ML work almost exclusively involves very heavy deep learning (transformers) and we primarily use PyTorch.

You will be working directly with our Founder/CEO (me!), who is leading the ML team, both as a manager and an individual contributor.

We would love to talk with anyone who has done research in text generation, information retrieval, or reinforcement learning for NLP tasks.

===

We are also hiring for non-technical (marketing) roles as well - see all of our open positions here: https://careers.glimpse.ai

Contact us by emailing alex@glimpse.ai with "HN" or applying on our career page listed above.

I can only guess either safety from misuse or leveraging it for money.

The former is being used as justification for the latter.

We have different servers for each. But the split is usually 80%/20% for inference/training. As our product grows in usage the 80% number is steadily increasing.

That isn't because we aren't training that often - we are almost always training many new models. It is just that inference is so computationally expensive!