NarmiNew York, NYBuild and ship reliable GenAI and agentic features from prototype to production in regulated fintech environments. Requires 5+ years software engineering experience with prior production GenAI deployments, strong backend skills, and building evaluation/monitoring infrastructure.
Shipping is the goal. The infrastructure you will help us build enables us to ship at pace in a regulated environment.
Nice to have
We are not looking for someone learning AI on the job. We’re looking for someone who has already seen it break and been there to fix it.
The expected annual base salary for this role is $160,000 - $254,000. Base salary is only part of your total compensation. In addition to base salary, you will receive an equity option grant, and are eligible for performance-based cash and equity bonuses. Compensation included in an offer will be commensurate with the candidate’s skills, experience and geographic location.
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.
Build and scale backend infrastructure powering agentic AI products including orchestration engines, RAG systems, evals infrastructure, and production AI workflows. Requires 4+ years distributed systems experience and deep Python plus Go/Java proficiency.
Builds and maintains ML infrastructure for training pipelines handling massive 3D data and real-time inference serving integrated with CAD software. Requires 3+ years experience with Python, PyTorch, ML orchestration tools, data versioning, and inference optimization.
Build and maintain ML infrastructure pipelines to deploy research models to production at scale, including CI/CD, A/B testing, monitoring, and optimization for low-latency voice AI serving. Requires 4+ years MLOps experience with Python, Docker, Kubernetes.