Jr. AI Engineer

What You'll Do

• Perform hands-on data annotation and quality control (labeling, reviewing, and correcting classification outputs) as a core member of the data science pipeline.
• Improve and scale the annotation process: identify bottlenecks, repetitive steps, and error sources, then fix them using Python, SQL, and agentic workflows.
• Build and run quality control checks to catch labeling errors, measure inter-annotator agreement, and identify systematic issues before production.
• Collaborate closely with data scientists and ML engineers to connect real-world performance to model improvements.
• Context switch across labeling, quality analysis, scripting, and process work as priorities evolve.
• Document QC processes and annotation guidelines to support team scaling and onboarding.

Requirements

• Solid programming ability with hands-on Python experience.
• Willingness to work with scripts, SQL, and data wrangling.
• Comfortable using or quickly ramping up on agentic development tools.
• Quality-first mindset with attention to detail in data.
• Dependable, adaptable, and able to context switch as priorities change.
• Energized by messy, real-world data and working with data-minded teammates.
• Self-directed and eager to grow in a fast-paced environment.

Nice-to-Haves

• Familiarity with feedback loops in ML systems and label quality's impact on model performance.
• Experience with annotation platforms (Label Studio, Prodigy, Scale, or custom systems).
• Knowledge of active learning or online learning approaches.
• Experience with SQL and building lightweight dashboards for quality metrics.
• Background in NLP or text classification workflows.