Founding Applied AI Engineer - Data

What you'll do

• Build production models and end-to-end pipelines that make fragmented enterprise data usable for AI, including serving, retraining, and monitoring.
• Integrate LLMs into production data pipelines and evaluate output quality and reliability.
• Develop internal data tooling and make technical decisions about data models, storage, orchestration, and infrastructure.

What they ask for

• Strong experience in some combination of Data Science, Data Engineering, and Machine Learning.
• Ability to design systems combining real-time streaming and batch/warehouse data under latency constraints.
• Fluency evaluating ML metrics, skepticism toward data quality, and ability to investigate raw records.
• Fluency with AI-assisted coding tools, sound independent verification, product instinct, and comfort working directly with customer teams.

Nice to have

• Experience building agentic or automated data-engineering tooling; hands-on experience with Databricks or Kafka.
• Experience with healthcare or other complex regulated enterprise data, LLM structured extraction pipelines, or startup, founding, or forward-deployed work.

Pay

$150K – $400K (base salary)

Tools

Databricks, Kafka, AUC, precision/recall, confusion matrices

AI focus

Build AI-ready data assets, production ML systems, and LLM-integrated pipelines; develop agentic or automated data tooling.