CoreView is hiring an AI Machine Learning Engineer

CoreView is looking for an AI/ML Engineer with professional experience shipping AI/ML systems to production, who's also genuinely into this stuff: someone who builds things on the side, follows the field because it's interesting, and has opinions earned by trying things. Your evidence can also be a side project, an open-source contribution, a fine-tuned model on Hugging Face, a homelab running locally, or an agent that got a bit out of hand. We care that it exists and that you can talk about it. What we're building We build tools that transform both our internal workflows and our customer-facing product. We explore new models, frameworks, and techniques as they emerge, run experiments, and bring into production what works. You'll work across the full AI stack, from model experimentation and retrieval to agentic systems and production features. Our stack includes a lot of stuff, and we stay open to whatever works best. Who we're looking for Someone solid on both GenAI/LLM application engineering and machine learning. Not a prompt-only integrator, not a pure researcher. An engineer who's curious about how things work under the hood and equally happy shipping them to users. - You've built agents, not just chatbots. You know what makes a multi-step workflow reliable: tool design, state, error handling, and knowing when to stop the loop - ML fundamentals you can apply. You're comfortable reasoning about models, data, and evaluation, and you can pick up new techniques as problems demand them. - AI-assisted development is part of how you work. You use AI coding assistants daily and have a real sense of where they shine, where they get in the way, and how to get the best out of them. - Curiosity that shows up in your work. You've broken an agentic workflow and fixed it. You've tested two retrieval strategies and have a take on which won. You've benchmarked local models on your own hardware. - Opinions you've earned. You know that no single technique is a silver bullet, and you can reason about trade-offs between agents, retrieval, fine-tuning, and prompting based on what you've tried. - You follow the field. You can tell us what you found interesting recently and why. - Hands-on experience with fine-tuning open-weight models, even on side projects, is a plus. What you'll work with Agentic systems, end-to-end RAG pipelines, evaluation pipelines, applied ML where it fits the problem, and AI coding assistants as part of your daily workflow.