We're hiring a Trust & Safety Engineer to join Runway's AI Safety and Responsibility team. Runway builds AI world models - general-purpose simulators for robotics, science, and storytelling — and this role exists to keep that safe as it reaches millions of users.
Stack: TypeScript API on ECS/Fargate, Python/PyTorch inference backend, Claude + Gemini for safety/moderation, ClickHouse for abuse signal detection, AWS + GCP, Terraform, Prometheus/Grafana.
It's a generalist role, hands-on across the full engineering lifecycle - from model pre-training through productionisation. We're looking for 3+ years production Python or TypeScript, comfort across the stack, and experience with data pipelines (ClickHouse a plus).
I've heard that LLMs can perform worse with these more efficient representations compared to e.g. JSON, because they've seen far fewer examples of them during training. Do you know how true that is?
I think you're being oversensitive, especially in the context Raycast adding support for Windows.
The whole purpose of Raycast is to improve productivity and UX, be that under macOS or Windows. It'd be a pretty shitty launch announcement if the blog post didn't mention the problem that they're trying to solve.
Edit: I'm not sure if the post was edited after my reply, but ATM there's no mention of BSODs - the closest I can see to a dig at Windows is:
> You know the feeling. Search that can't find your files. Apps buried in menus. Simple tasks that take too many clicks. Your computer should be faster than this. It should feel like everything is at your fingertips. That’s why we built Raycast.
I imagine it may not be a proxy in the true sense, but a headless browser that's "proxying" the application process rather than the network traffic itself.
The docs for the toolchain he implemented (https://github.com/taviso/rarvmtools) allude to a number of bugs, but doesn't sound (??) like they're related to this vulnerability.
> "After trying four methods, I got so tired of this problem that it was time just to choose something, make it into a usable tool, and announce the solution"
These guys are building foundational models for this purpose: https://reveng.ai/. The results are quite compelling, and they have plugins for your favourite reverse engineering tools.
It'd be interesting to learn more about how this line was identified - was it by eye, or is there some neat analysis that was (or could be) performed to find it?
I'm curious as to how much paperwork was involved here. Was the pickle grounding out the transmitter and (presumably) interrupting the broadcast not an issue?
We're hiring a Trust & Safety Engineer to join Runway's AI Safety and Responsibility team. Runway builds AI world models - general-purpose simulators for robotics, science, and storytelling — and this role exists to keep that safe as it reaches millions of users.
Stack: TypeScript API on ECS/Fargate, Python/PyTorch inference backend, Claude + Gemini for safety/moderation, ClickHouse for abuse signal detection, AWS + GCP, Terraform, Prometheus/Grafana.
It's a generalist role, hands-on across the full engineering lifecycle - from model pre-training through productionisation. We're looking for 3+ years production Python or TypeScript, comfort across the stack, and experience with data pipelines (ClickHouse a plus).
Apply here: https://jobs.ashbyhq.com/runway-ml/c3ebd871-ea78-4b71-80a2-7..., or feel free to reach out with any questions (info in bio).
We're also hiring for a bunch of other roles, across Engineering, Research, GTM, Marketing, Finance, Legal: https://jobs.ashbyhq.com/runway-ml?utm_source=kW6D9goed3