Why Traditional Hiring Methods Fall Short
Standard software engineering interviews — LeetCode problems, whiteboard algorithms, system design for web apps — miss what matters in AI engineering. You need to evaluate: understanding of ML concepts and their practical application, experience with the messiness of real data and models, ability to make tradeoff decisions (accuracy vs. latency, cost vs. quality), and production deployment experience versus notebook-only work.
The Four-Stage Evaluation Framework
Stage 1: Portfolio and Experience Screen (30 minutes)
Review their GitHub, published work, or project portfolio. Look for: production deployments (not just Kaggle competitions), contributions to open-source AI projects, technical blog posts or documentation, and diversity of tools and frameworks used. Ask them to walk through their most complex project.
Stage 2: Technical Deep-Dive (60 minutes)
Conduct a conversational technical interview focused on their domain. For ML engineers, discuss model selection, feature engineering, and evaluation metrics. For LLM engineers, explore prompt design, RAG architecture, and fine-tuning decisions. For MLOps engineers, probe their deployment pipeline design and monitoring approach.
Stage 3: Practical Assessment (Take-Home, 4-6 hours)
Give a realistic, scoped project that mirrors actual work. Provide a dataset and problem statement, and ask them to build a solution including: data exploration and preprocessing, model or pipeline implementation, evaluation and results documentation, and a brief writeup explaining their approach and tradeoffs.
Stage 4: Team Fit and Communication (45 minutes)
Evaluate their communication skills, especially for remote roles. Can they explain their technical decisions clearly? Do they ask good questions? Are they comfortable with async communication? For LatAm candidates, assess English fluency in a natural conversation, not a scripted test.
Red Flags to Watch For
Be wary of candidates who: can't explain their own code or model choices, have only worked with toy datasets and tutorials, dismiss evaluation and testing as unimportant, are unwilling to discuss failures or limitations, or claim expertise in every AI framework and tool.
How South Pre-Vets Candidates
South's screening process covers all four stages before candidates ever reach you. We evaluate technical depth, English communication, production experience, and remote work readiness. You receive candidates who've already passed a rigorous bar — your interview process confirms fit rather than screening basics.
