Machine Learning Engineer (AI)

TL;DR

Machine Learning Engineer (AI): Architecting and developing Chakra, an AI interviewer, end-to-end, focusing on agent design, conversation management, and real-time response evaluation. Focus on building systems that ensure interview consistency at scale and designing evaluation pipelines to measure interview quality and candidate experience.

Location: Hybrid in Santa Clara, CA

Salary: $120,000 - $235,000, plus a target 10% annual bonus

Company

hirify.global helps companies like NVIDIA, Amazon, and Microsoft hire and upskill the next generation of developers based on skills, not pedigree.

What you will do

• Architect and develop Chakra end to end: the agent design, conversation management, real-time response evaluation, scoring methodology, and report generation.
• Build systems that ensure interview consistency at scale.
• 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 and instrumenting how well the system meets that bar.
• 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, considering the conversation architecture, the evaluation model, the serving infrastructure, and the candidate experience as one problem.
• You care about the quality bar at the level of a user who depends on the output, not just a researcher measuring aggregate metrics.

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.

Culture & Benefits

• You are energized by the full scope of a hard product problem, from model architecture through the conversation a candidate actually has.
• You hold the product bar as high as the technical bar and want to build something that works extraordinarily well for every single person who uses it.
• Comprehensive package of cash and non-cash benefits.
• Equity (stock options).