You will build and ship LLM-powered applications and AI agent workflows by designing systems for reasoning, planning, and tool use. Additionally, you will develop evaluation frameworks to measure AI quality and optimize systems for performance, latency, and cost.
Architect and build large-scale ML systems covering training, inference, and deployment. Set technical standards for ML infrastructure and optimize GPU performance for production-grade systems.
Build end-to-end product features and design agent workflows that handle planning, tool use, and recovery. Integrate LLMs and external tools to create reliable, production-grade AI systems with low latency.
Build and improve core ML components across data, training, evaluation, and inference for real production systems. Debug model issues and iterate on features based on real-world user feedback and production constraints.
The role involves translating research into scalable, production-grade ML systems and owning the end-to-end execution of data pipelines and inference architecture. You will be responsible for fine-tuning models and optimizing GPU performance to ensure reliability and low latency in real-world products.
The role involves building and owning critical machine learning subsystems from data preparation to production deployment. You will collaborate cross-functionally to turn research ideas into scalable, reliable AI products while mentoring other engineers.
Own the end-to-end execution of ML systems, translating research into scalable production-grade solutions. Responsible for data pipelines, model fine-tuning, and optimizing inference systems for latency and cost.
The Software Engineer will build and maintain cross-platform desktop applications using Electron, focusing on reliability and real-time performance. Responsibilities include integrating AI-powered features and optimizing system behavior across macOS and Windows environments.
