Reza Shabani: evaluation

Transcript context

…So plans for the future, like hard problems that you wanna solve? Maybe like what, what do you like what kind of people that you're hiring on your team? Yeah. So We are, we're currently hiring for for two different roles on, on my team. Although we, you know, welcome applications from anyone that, that thinks they can contribute in, in this area. Replica tends to be like a, a band of misfits. And, and the type of people we work with and, and have on our team are you know, like just the, the perfect mix to, to do amazing projects like this with very, very few people. Right now we're hiring for the applied a applied to AI ml engineer. And so, you know, this is someone who's. Creating data pipelines, processing the data at scale creating runs and and training models and you know, running different variations, testing the output running human evals and, and solving a, a ton of the issues that come up in the, in the training pipeline from beginning to end. And so, you know, if you read the, the blog post we'll be going into, we'll be releasing additional blog posts that go into the details of, of each of those different sections. You know, just like tokenized training is incredibly complex and you can write, you know, a whole series of blog posts on that. And so the, those types of really challenging. Engineering problems of how do you sample this data at, at scale from different languages in different RDS and pipelines and, and feed them to you know, sense peace tokenize to, to learn. If you're interested in working in that type of, of stuff we'd love to speak with you. And and same for on the inference side. So like, if you wanna figure out how to make these models be lightning fast and optimize the the transformer layer to get like as much out of out of inference and reduce latency as much as possible you know, you'd be, you'd be joining our team and working alongside. Bradley, for example, who was like he, I always embarrass him and he's like the most humble person ever, but I'm gonna embarrass him here. He was employee number seven at YouTube and Wow. Yeah, so when I met him I was like, why are you here? But that's like the kind of person that joins Relet and, you know, he, he's obviously seen like how to scale systems and, and seen, seen it all. And like he's like the type of person who works on like our inference stack and makes it faster and scalable and and is phenomenal. So if you're just a solid engineer and wanna work on anything related to LLMs In terms of like training inference, data pipelines the applied AI ML role is, is a great role. We're also hiring for a full stack engineer. So this would be someone on my team who does both the model training stuff, but, but is more oriented towards bringing that AI to to users. And so that could mean many different things. It could mean you know, on the front end building the integrations with the workspace that allow you to, to receive the code completion models. It means working on Go rider chats, like the conversational ability between. ow, on the front end building the integrations with the workspace that allow you to, to receive the code completion models. It means working on Go rider chats, like the conversational ability between. Ghost Writer and what you're trying to do, building the various agents that we want replica to have access to. Creating embeddings to allow people to ask questions about you know, docs or or, or their own projects or, or other teams, projects that they're collaborating with. All of those types of things are in the, in the kind of full stack role that that I'm hiring for on my team as well. Perfect. Awesome.…

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