Trycua
Overview
Cua is building infrastructure for safely and scalably running general AI agents on real computers and apps. With 9k+ GitHub stars in just 4 months and backing from Y Combinator, we’re advancing agentic AI from research to production. We’re hiring a Research Engineer to help develop and scale advanced multi‑modal AI agents — working across model training, agent benchmarking, and real-world deployment. WHAT YOU’LL DO
You’ll sit at the intersection of applied research and engineering, helping us build, evaluate, and ship the next generation of generative agent systems. Example work includes: Designing and running experiments on commercial/open‑source LLMs (e.g., OpenAI, LLaMA, Qwen) Building scalable pipelines for training, fine‑tuning, and evaluating multi‑modal agents Developing tools and benchmarks to test agent reasoning, control, and performance Improving infrastructure for deploying agentic AI models across OS environments Supporting research‑to‑production workflows for internal and external users WHAT WE’RE LOOKING FOR
Strong experience with generative AI, LLMs, and agentic systems Hands‑on with commercial and open‑source models at scale Proficiency in Python and PyTorch (C++/Java a plus) Experience with data curation pipelines and multi‑modal training workflows Comfortable designing experiments, testing infra, and pushing code into prod Exposure to cloud compute (AWS/GCP), APIs, structured/unstructured data Open‑source or competition experience is a plus LOGISTICS
Full‑time, remote‑friendly (SF‑based team preferred) Role blends fast‑paced engineering with cutting‑edge research Work used by thousands of developers building with Cua APPLY
CV + GitHub or portfolio Short note on a project or experiment you’ve recently led trycua.com We’re committed to building a diverse, inclusive team — all backgrounds welcome.
#J-18808-Ljbffr
Cua is building infrastructure for safely and scalably running general AI agents on real computers and apps. With 9k+ GitHub stars in just 4 months and backing from Y Combinator, we’re advancing agentic AI from research to production. We’re hiring a Research Engineer to help develop and scale advanced multi‑modal AI agents — working across model training, agent benchmarking, and real-world deployment. WHAT YOU’LL DO
You’ll sit at the intersection of applied research and engineering, helping us build, evaluate, and ship the next generation of generative agent systems. Example work includes: Designing and running experiments on commercial/open‑source LLMs (e.g., OpenAI, LLaMA, Qwen) Building scalable pipelines for training, fine‑tuning, and evaluating multi‑modal agents Developing tools and benchmarks to test agent reasoning, control, and performance Improving infrastructure for deploying agentic AI models across OS environments Supporting research‑to‑production workflows for internal and external users WHAT WE’RE LOOKING FOR
Strong experience with generative AI, LLMs, and agentic systems Hands‑on with commercial and open‑source models at scale Proficiency in Python and PyTorch (C++/Java a plus) Experience with data curation pipelines and multi‑modal training workflows Comfortable designing experiments, testing infra, and pushing code into prod Exposure to cloud compute (AWS/GCP), APIs, structured/unstructured data Open‑source or competition experience is a plus LOGISTICS
Full‑time, remote‑friendly (SF‑based team preferred) Role blends fast‑paced engineering with cutting‑edge research Work used by thousands of developers building with Cua APPLY
CV + GitHub or portfolio Short note on a project or experiment you’ve recently led trycua.com We’re committed to building a diverse, inclusive team — all backgrounds welcome.
#J-18808-Ljbffr