DS ML Intern A builder mindset is the core of this role and where youll spend most of your time But were 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 Were looking for someone energized by the breadth not someone who wants to stay in their lane What youll work on Evaluation systems for AI features Help build the eval backbone our AI features ship against failure taxonomies LLMasjudge 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 handson 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 realworld signals into scores you can actually trust grounded in real statistical rigor not vibes Help move heuristicdriven 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 were looking for Must have Currently pursuing or recently completed a degree in CS DS ML or a related field Some handson DS ML experience coursework personal projects research or a prior internship where youve 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 builders 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 entitymatching or recordlinkage Interest in developerproductivity code analytics or DevEx data We aspire to create an inclusive culture of diverse people not just because its the right thing to do but because heterogeneity inspires us and is more fun We employ people solely on merit and do not discriminate against any employee or applicant because of race creed color religion gender sexual orientation gender identity expression national origin disability age genetic information marital status pregnancy or related condition including breastfeeding or any other basis protected by law Experience Level Entry Level DS ML Intern A builder mindset is the core of this role and where youll spend most of your time But were 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 Were looking for someone energized by the breadth not someone who wants to stay in their lane What youll work on Evaluation systems for AI features Help build the eval backbone our AI features ship against failure taxonomies LLMasjudge 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 handson 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 realworld signals into scores you can actually trust grounded in real statistical rigor not vibes Help move heuristicdriven 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 were looking for Must have Currently pursuing or recently completed a degree in CS DS ML or a related field Some handson DS ML experience coursework personal projects research or a prior internship where youve 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 builders instinct genuinely curious about product decisions backend or frontend not just the modeling layer Some exposure to LLMs p