Machine Learning Engineer, Chakra
HackerRank
Hybrid in Bangalore, IndiahybridPosted 3 months ago
Skill Required
EngineeringMachine Learning EngineerMachine LearningGenerative AI
Key highlights
- Build and ship agentic or conversational AI systems in production.
- Focus on LLM reliability and systemic address of model breakdown.
- Involves fine-tuning and RLHF workflows.
- Requires full-stack contribution including data pipelines and model serving.
- Mission-driven work for a platform with 30M+ assessed developers.
Role overview
HackerRank is transforming technical hiring in the age of AI, moving beyond traditional pedigree-based assessment to help over 2,500 companies build engineering teams. This role centers on Chakra, an AI interviewer designed to conduct consistent, probing, and fair technical interviews at scale by simulating high-quality human judgment.
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, including the infrastructure that makes the 200,000th interview as coherent as the first.
- Design evaluation and benchmarking pipelines that measure interview quality, candidate experience consistency, and report defensibility.
- 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 by defining what a good interview looks like, instrumenting system performance, and closing the gap systematically.
- Work across the full stack: data pipelines, model serving, latency constraints, and the product experience the candidate encounters.
Requirements
- Built and shipped agentic or conversational AI systems in production, not just prototypes.
- Strong intuition for where LLM behavior breaks down under real-world conditions and how to address it systematically.
- Systems-oriented thinking regarding conversation architecture, evaluation models, serving infrastructure, and candidate experience as one cohesive problem.
- Commitment to a high-quality bar at the level of a user who depends on the output, beyond just measuring aggregate metrics.
- Energized by the full scope of a hard product problem, from model architecture through the end-user conversation experience.
- Hold the product bar as high as the technical bar, aiming to build something that works extraordinarily well for every single user.
Nice to have
- 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.
Additional details
- HackerRank is a proud equal employment opportunity and affirmative action employer, providing equal opportunity to everyone based on individual performance and qualification.
- HackerRank does not discriminate based on race, religion, national origin, gender identity or expression, sexual orientation, age, marital, veteran, or disability status.
- All information is kept confidential according to EEO guidelines.
- Recruiters use @hackerrank.com email addresses.
- HackerRank never asks for payment or credit check information to apply, interview, or work at the company.