Machine Learning Engineer
HackerRank
full-time
Posted on:
Location Type: Hybrid
Location: Santa Clara • California • United States
Visit company websiteExplore more
Salary
💰 $120,000 - $235,000 per year
About the role
- 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 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. Define what a good interview looks like, instrument how well the system meets that bar, and close the gap systematically.
- Work across the full stack: data pipelines, model serving, latency constraints, and the product experience the candidate actually encounters.
Requirements
- 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.
- You think in systems. The conversation architecture, the evaluation model, the serving infrastructure, and the candidate experience are one problem to you.
- You care about the quality bar at the level of a user who depends on the output, not just a researcher measuring aggregate metrics.
Benefits
- a target 10% annual bonus tied to individual and company performance
- equity (stock options)
- a comprehensive package of cash and non-cash benefits
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
Chakraconversation managementreal-time response evaluationscoring methodologyreport generationfine-tuningRLHF workflowsdata pipelinesmodel servingevaluation model
Soft Skills
system thinkingquality assuranceintuition for LLM behaviorproblem-solvinguser experience focus