Staff AI/ML Engineer - Build Production Reasoning Systems

APADCode; United States; Hybrid; USD 180,000 - 240,000; Full time

Benefits offered by this job

Equity opportunities

Flexible working arrangements

Equipment and development support

Direct collaboration with founding팀

Job summary

APADCode is an early-stage AI startup redefining how engineering talent is evaluated. We’re building an AI-powered interview platform that measures reasoning and problem-solving, not just code accuracy.

You’ll own significant technical areas and help turn research into production systems. You’ll work on pipelines for semantic analysis, behavioral signals, and evaluation, with a focus on bias, reliability, and scalability.

Qualifications

• Strong software engineering fundamentals and hands-on experience building production ML or AI systems.
• Proficiency in Python and modern machine learning frameworks.
• Practical experience with LLMs, NLP, model evaluation, or applied ML.
• Solid understanding of data modeling, statistics, experimentation, and ML system design.

Responsibilities

• Design and improve AI systems for extracting structured reasoning signals from technical interviews.
• Build LLM-powered pipelines for semantic analysis, behavioral feature extraction, and evaluation.
• Develop scoring, calibration, and benchmarking methods grounded in measurable evidence.
• Design experiments and evaluation frameworks to measure model accuracy, reliability, bias, and consistency.
• Build scalable inference and data pipelines, balancing model quality, latency, and cost.
• Collaborate on product architecture and help define the technical direction of our AI platform.

Skills

Production ML systems

Python

ML frameworks

LLMs

NLP

Data modeling

Experimentation

System design

Job description

APADCode is an early-stage AI startup redefining how engineering talent is evaluated. We’re building an AI-powered interview platform that measures reasoning and problem-solving, not just code accuracy.

You’ll own significant technical areas and help turn research into production systems. You’ll work on pipelines for semantic analysis, behavioral signals, and evaluation, with a focus on bias, reliability, and scalability.