DS/ML Intern

• A builder mindset is the core of this role and where you'll spend most of your time. But we're a small team building a whole product, not a research lab. The best person here treats ML systems as their primary craft while staying willing to do whatever the product needs — thinking through the product itself, shipping backend or frontend code, untangling data pipelines. We're looking for someone energized by the breadth, not someone who wants to stay in their lane.
• What you'll work on
• Evaluation systems for AI features

Help build the eval backbone our AI features ship against — failure taxonomies, LLM-as-judge rubrics, golden datasets, calibration against human judgment.

Learn what it takes to keep automated scores honest as models and prompts change. A feature with no eval has no quality floor.

Model routing & inference economics

Get hands-on with how we route work across models — balancing cost, quality, and latency per task.

Help run the experiments that justify those choices and catch regressions.

Scoring, measurement & signal quality

Work on turning noisy, real-world signals into scores you can actually trust — grounded in real statistical rigor, not vibes.

Help move heuristic-driven approaches toward calibrated, monitored systems.

MLOps & production

Get exposure to the full lifecycle — feature pipelines, model versioning, rollout, monitoring for drift and silent quality decay.

Work alongside engineering to see how models get served reliably at low latency.

• What we're looking for
• Must have

Currently pursuing or recently completed a degree in CS, DS, ML, or a related field.

Some hands-on DS/ML experience — coursework, personal projects, research, or a prior internship — where you've built and run something end to end, not just notebooks.

Comfort with Python and working SQL knowledge.

Basic grounding in applied statistics — you can explain what a metric means and when it might be misleading.

A builder's instinct — genuinely curious about product decisions, backend, or frontend, not just the modeling layer.

Some exposure to LLMs — prompting, using APIs, or experimenting with model behavior.

Nice to have

Any exposure to evaluation or observability tooling for LLM features.

Coursework or projects in information retrieval, entity-matching, or record-linkage.

Interest in developer-productivity, code analytics, or DevEx data.