Candidate completes adaptive challenges in a live environment. WallFace™ monitors authenticity across 7 layers throughout.
AI evaluates on multiple dimensions — correctness, approach, code quality, edge case handling. No pass/fail. No black box.
Plain-language summary delivered to the recruiter in under 5 minutes. Skill ratings, flags for follow-up, suggested interview questions.
The recruiter reviews the report and makes the call. Advance, hold, or pass. The AI surfaces the signal. You decide.
Candidate completes adaptive challenges in a live environment. WallFace™ monitors authenticity across 7 layers throughout.
AI evaluates on multiple dimensions — correctness, approach, code quality, edge case handling. No pass/fail. No black box.
Plain-language summary delivered to the recruiter in under 5 minutes. Skill ratings, flags for follow-up, suggested interview questions.
The recruiter reviews the report and makes the call. Advance, hold, or pass. The AI surfaces the signal. You decide.
The ML Engineer assessment evaluates a candidate's ability to build, deploy, and maintain machine learning systems. It covers model training, Python ML frameworks, feature engineering, MLOps, and production deployment.
A report with ML-specific skill ratings, framework proficiency verification, production-readiness signals, and interview questions focused on the candidate's approach to model monitoring and iteration.
Agencies placing ML engineers at companies building AI products. TA teams making their first ML hire who need a structured evaluation without an existing data science team to run technical screens.