Applied AI Engineer
Build and ship full-stack AI projects including AI agents, RAG, structured extraction, and LLM infrastructure. Requires proficiency in full-stack development, backend systems, cloud infrastructure, and production LLM experience.
Design and deploy end-to-end AI systems powering real products. Own the full intelligence stack including retrieval, agent orchestration, evals, and governance. Requires Python, LLM experience, and production AI system building.
Build and ship full-stack AI projects including AI agents, RAG, structured extraction, and LLM infrastructure. Requires proficiency in full-stack development, backend systems, cloud infrastructure, and production LLM experience.
Builds ML tools, infrastructure, and manages large datasets for end-to-end autonomy research and productionizing self-driving software. Works with AI research and engineering teams to scale GPU compute, data, and evaluation systems. Requires strong software generalist skills across ML stack.
Optimizes ML models for performance on embedded compute platforms in ADAS/AD stacks, focusing on inference efficiency, pruning, quantization, and profiling across GPU/CPU/SoC architectures. Requires 3+ years experience with deep learning frameworks and embedded systems.
Research Scientist II building and improving fraud risk models and scam detection systems using audio, behavioral, and metadata signals. Requires an advanced degree and 3+ years of applied ML experience with Python and modern ML frameworks.
As an AI Engineer, you will own the full AI engineering lifecycle, from design to optimization, for Snowflake Database Engineering products. You will build agentic workflows, coding harnesses, and evaluation pipelines, working with a high-powered engineering team.