DS/ML Intern

About the role

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. Bangalore

Key responsibilities

• check_circleCollaborate with the team on day-to-day project tasks
• check_circleLearn tools and processes used by the organization
• check_circleDocument work and participate in team meetings
• check_circleSupport quality checks and continuous improvement

Requirements

• check_circleHelp build the eval backbone our AI features ship against — failure taxonomies, LLM-as-judge rubrics, golden datasets, calibration against human judgment.
• check_circleLearn what it takes to keep automated scores honest as models and prompts change. A feature with no eval has no quality floor.
• check_circleGet hands-on with how we route work across models — balancing cost, quality, and latency per task.
• check_circleHelp run the experiments that justify those choices and catch regressions.
• check_circleWork on turning noisy, real-world signals into scores you can actually trust — grounded in real statistical rigor, not vibes.
• check_circleHelp move heuristic-driven approaches toward calibrated, monitored systems.
• check_circleGet exposure to the full lifecycle — feature pipelines, model versioning, rollout, monitoring for drift and silent quality decay.
• check_circleWork alongside engineering to see how models get served reliably at low latency.
• check_circleCurrently pursuing or recently completed a degree in CS, DS, ML, or a related field.
• check_circleSome 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.
• check_circleComfort with Python and working SQL knowledge.
• check_circleBasic grounding in applied statistics — you can explain what a metric means and when it might be misleading.

Skills & keywords

Benefits & perks

• check_circleMentorship
• check_circleCertificate of completion
• check_circleFlexible work arrangement where applicable