Want to know if this job is worth applying to?
서울특별시, 대한민국
Hybrid
There are over 5 billion users using basic applications today such email, notes, tasks, calendar and they're not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting.
Our first application is an agent-first mobile communication app. We believe building applications that users use daily for communication, coordination and collaboration needs will help integrate AI into their lives. The net effect is to eliminate misinformation, achieve low coordination costs, enable high quality tasks and collaboration at scale with minimal or little prompting.
Design and build ML systems covering data processing, model training, evaluation, inference and deployment
Develop training and fine-tuning pipelines for large language models
Build evaluation frameworks to measure model quality, reliability and performance
Optimise inference systems for latency, throughput, GPU utilisation and cost
Develop data pipelines for collecting, processing and validating training and evaluation datasets
Deploy, monitor and troubleshoot ML models and infrastructure in production
Make architecture decisions and guide engineers through technical design and code reviews
Work with research and application engineering teams to improve model performance in production
8+ years of software engineering or machine learning engineering experience, including production ML systems
Strong Python programming skills and hands-on experience with PyTorch or JAX
Experience training, fine-tuning and evaluating large machine learning models
Experience building and operating GPU-based training or inference systems
Strong understanding of distributed computing, data pipelines and ML infrastructure
Experience optimising model performance, inference latency and resource utilisation
Ability to design ML system architecture, review code and resolve complex production issues
Experience providing technical leadership to ML engineers
Experience with large language models, transformers and distributed model training
Experience with inference frameworks such as vLLM or TensorRT-LLM
Experience with synthetic data generation, data curation and model evaluation pipelines
Experience deploying ML systems for consumer applications