AI Engineer - Data Intelligence

Clarium

2d•$150,000 - $180,000•Remote

About The Position

Clarium is seeking an AI Engineer to join their Data Intelligence team. This role focuses on building and maintaining components of Clarium's master data enrichment pipeline, which classifies and enriches product data for their AI-powered platform, Astra OS. The position offers a rare early-career opportunity to learn quickly and take ownership of significant work from day one, working closely with experienced engineers and data scientists. The goal is to make the data enrichment layer faster, smarter, and more reliable to support Clarium's growth and provide actionable insights to healthcare supply chain teams.

Requirements

Strong Python skills and a track record of writing production code, not just scripts or notebooks
Strong SQL, including complex joins, window functions, performance tuning, and data modeling
Comfort working in ambiguous environments; you can scope a problem, make a plan, and execute without hand-holding
A genuine, non-negotiable commitment to data quality; you treat silent bugs as real failures
Ability to go deep on an unfamiliar domain and develop meaningful expertise over time

Nice To Haves

Experience with LLM integrations, prompt evaluation, or classification at scale
Familiarity with eval frameworks such as Braintrust, Promptfoo, or equivalent
Prior work in healthcare, supply chain, or another domain where data quality has direct operational consequences

Responsibilities

Build and maintain components of Clarium's master data enrichment pipeline, the system that classifies and enriches every product flowing through our platform
Design and own classification and entity resolution workflows that combine deterministic logic and LLMs for production data processing
Build and operate evaluation harnesses, label sets, and regression suites (we use Braintrust) to measure and improve pipeline quality with confidence
Write production Python and SQL; the majority of your time will be spent in code, not in configuration tools
Analyze complex datasets using statistics and ML to surface actionable insights and inform pipeline improvements
Proactively audit data for quality issues; find the problems no one else has noticed yet, diagnose root causes, and ship fixes