data
Posted 1 weeks agoMachine Learning Engineer, Chakra
at HackerRank
IndiaHybrid
Responsibilities
- Architect and develop Chakra end to end: the agent design, conversation management, real-time response evaluation, scoring methodology, and report generation.
- Build the systems that ensure interview consistency at scale. Not just model capability, but the infrastructure that makes the 200,000th interview as coherent as the first.
- Design evaluation and benchmarking pipelines that measure interview quality, candidate
- Build fine-tuning and RLHF workflows to push model judgment past what off-the-shelf models deliver for this specific task.
- Own the quality bar. Define what a good interview looks like, instrument how well the system meets that bar, and close the gap systematically.
Requirements
- Software has entered an era where humans and AI build side by side.
- The developer's job is shifting from writing code to directing AI agents, and hiring needs to catch up.
- Chakra is our bet on what the next generation of that looks like: an AI interviewer built for a world where the interview itself has to be as intelligent as the candidates it is evaluating. Open Problem
- Chakra is an AI interviewer.
- You have built and shipped agentic or conversational AI systems in production, not just prototypes.
- You have a strong intuition for where LLM behavior breaks down under real-world conditions and how to address it systematically.
- Experience building multi-turn conversational agents or interview-style AI systems.
- Worked with RLHF, Constitutional AI, or preference-based fine-tuning methods.
- Background in dialogue systems, conversational evaluation, or rubric-based scoring.
- Publications or contributions in agentic AI, LLM reliability, or evaluation of generative systems. You will thrive in this role if
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