Member of Technical Staff - AI/ML

About APADCode

APADCode is an early-stage AI startup rethinking how engineering talent is evaluated. We're building an AI-powered technical interview platform that goes beyond whether a candidate writes the right code to understand how they reason, solve problems, and make engineering decisions.

Our platform is already being used in real hiring workflows. Now we're building the intelligence layer that makes technical evaluation more measurable, consistent, and trustworthy.

About the Role

We're looking for a Member of Technical Staff to help build the core AI intelligence behind APADCode. You'll work closely with the founding team on problems at the intersection of LLMs, reasoning analysis, machine learning, and evaluation systems.

This is a hands-on role with significant technical ownership. You'll help turn early research and prototypes into reliable production systems while shaping how we measure and validate engineering reasoning.

What You'll Do

• 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.

What We Look For

• 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.

• Ability to navigate ambiguous problems, challenge assumptions, and independently drive technical decisions.

• A strong bias toward building, measuring, and improving systems in the real world.

Nice to Have

• Experience with agentic AI, structured outputs, model serving, or inference optimization.

• Background in ranking, representation learning, behavioral analytics, or human-in-the-loop evaluation.

• Experience building ML infrastructure or products from an early stage.

What We Offer

• Competitive compensation and meaningful early-stage equity opportunities.

• High ownership and direct influence over the product's technical direction.

• Flexible working arrangements and paid time off.

• Health and wellness benefits appropriate to your location.

• Equipment and professional development support.

• Direct collaboration with the founding team, minimal bureaucracy, and the opportunity to build foundational technology from the ground up.

We're looking for people who care about solving difficult problems and shipping systems that actually work. If that sounds like you, we'd love to talk.