Cybersecurity/AI seed startup | Founding AI Engineer | Bay Area | Full-time
We're seed-staged, 3 people, building an AI for cybersecurity, looking for a founding AI engineer who wants to learn/apply SOTA techniques for AI. Ideal background is experience building production agentic AI systems (by this I mean something like Simon's definition: https://simonwillison.net/2025/Sep/18/agents/ definition) who also likes to think about WHAT to build and not just how.
We are a Golang/Python shop (although I'm not sure that matters so much any more).
Securing LLMs is just structurally different. The attack space is "the entirety of the human written language" which is effectively infinite. Wrapping your head around this is something we're only now starting to appreciate.
In general, treating LLM outputs (no matter where) as untrusted, and ensuring classic cybersecurity guardrails (sandboxing, data permissioning, logging) is the current SOTA on mitigation. It'll be interesting to see how approaches evolve as we figure out more.
Polar Sky | Bay Area | Full-time | Founding AI Engineer
Generative AI is rewriting how organizations use data, and breaking traditional security models in the process. We’re a team of cybersecurity, AI, and systems experts building the foundation for secure, trustworthy AI in the enterprise.
We're looking for a Founding AI Engineer who loves building with AI -- crafting context pipelines, integrating and evaluating LLMs into production systems, and delivering AI-native product experiences. You'll work on all parts of Polar Sky, from the data and eval systems to the reasoning, retrieval, and orchestration systems.
Polar Sky | Founding AI Lead | Bay Area/Seattle | Hybrid/Onsite | Full-time
We're a well-funded, pre-seed cybersecurity startup focused on data security. I'm looking for a founding AI lead with experience in fine-tuning LLMs (expertise around RL + reasoning models a big plus). This person would own the full AI stack from data to training to eval to test-time compute.
Who's a good fit:
* If you've always thought about starting a company, but for whatever reason (funding, life, idea), this is a great opportunity to be part of the founding team. We're 2 people right now.
* You enjoy understanding customer problems and their use cases, and then figuring out the best solution (sometimes technical, sometimes not) to their problems.
* You want to help figure out what a company looks like in this AI era.
Polar Sky | Founding AI Lead | Bay Area/Seattle | Hybrid/Onsite | Full-time
We're a well-funded, pre-seed cybersecurity startup focused on data security. I'm looking for a founding AI lead with experience in fine-tuning LLMs (expertise around RL + reasoning models a big plus). This person would own the full AI stack from data to training to eval to test-time compute.
Who's a good fit:
* If you've always thought about starting a company, but for whatever reason (funding, life, idea), this is a great opportunity to be part of the founding team. We're 2 people right now.
* You enjoy understanding customer problems and their use cases, and then figuring out the best solution (sometimes technical, sometimes not) to their problems.
* You want to help figure out what a company looks like in this AI era.
In this analogy, Dynamo is most definitely not like Django. It includes inference aware routing, KV caching, etc. -- all the stuff you would need to run a modern SOTA inference stack.
This is really interesting. For SOTA inference systems, I've seen two general approaches:
* The "stack-centric" approach such as vLLM production stack, AIBrix, etc. These set up an entire inference stack for you including KV cache, routing, etc.
* The "pipeline-centric" approach such as NVidia Dynamo, Ray, BentoML. These give you more of an SDK so you can define inference pipelines that you can then deploy on your specific hardware.
It seems like LLM-d is the former. Is that right? What prompted you to go down that direction, instead of the direction of Dynamo?
The blog post was a little unclear, so my summary was:
- They used QwQ to generate training data (with some cleanup using GPT-4o-mini)
- The training data was then used to FT Qwen2.5-32B-Instruct (non-reasoning model)
- Result was that Sky-T1 performs slightly worse than QwQ but much better than Qwen2.5 on reasoning tasks
There are a few dismissive comments here but I actually think this is pretty interesting as it shows how you can FT a foundation model to do better at reasoning.
I took a brief look (~5 minutes). My $0.02 is that it's not clear what problem you're trying to solve. I get what some of the features do (e.g., templated prompts) but it would be v helpful to have an example of how you actually use magentic, versus the non-magentic way. It feels like a lot of syntactic sugar, if I'm being honest (not a bad thing, but something you might want to be clear about, if that's the case.)
(author here) I didn't put this in my post, but one of my favorite moments was when I read some of the LlamaIndex source code which pointed to the GitHub commit where they copied the code verbatim from LangChain. (LangChain is MIT-licensed, so it's OK, but I still thought it was funny!)
Not a bad move by Red Hat. Red Hat lost the battle of the cloud to Azure, AWS, and Google, but AI is still a nascent space. vLLM's deployment model fits neatly into Red Hat's traditional on-premise / support-centric business model.
I'm running ollama, but it's still slow (it's actually quite fast on my M2). My working theory is that with standard cloud VMs, memory <-> CPU bandwidth is an issue. I'm looking into vLLM.
And as to sidestepping inference, I can totally do that. But I think it's so much better to be able to ask the LLM a question, run a vector similarity search to pull relevant content, and then have the LLM summarize this all in a way that answers my question.