Core Competency of Algorithm Engineer
├── Technical Depth
│ ├── Algorithm Intuition
│ ├── Math Foundation
│ ├── Deep Learning Theory
│ └── Diagnostic Skill
├── Engineering Knowledge
│ ├── Data Engineering
│ ├── Deployment & Runtime Optimization
│ ├── LLM / Prompt Engineering
│ └── Tool Proficiency
└── Business Knowledge
├── Problem Translation
├── Metric Alignment
├── Industry Domain Knowledge
└── Decision Impact Awareness
Core Competence = Technical Depth × Engineering Capability × Business Understanding.
Each factor multiplies the others. Weakness in one reduces the total.
I. Technical Depth
Concept Card: Knowing the “Why”, Not Just the “What”
Understanding the internal principles behind algorithms and models. Instead of using for shortest path, know when to use dynamic programming vs. greedy approach.
Concept Card: Mathematical Foundations
Deep knowledge of linear algebra, probability, calculus, and perform optimization to understand ML/DL principles. It is always better to know why regularization leads to sparse weights; how gradient descent navigates convex vs. non-convex functions.
Concept Card: AI/ML Principles
Must possess the In-depth knowledge of model architectures, training dynamics, overfitting, generalization, and hyperparameter tuning.
Heuristic Questions
- What trade-offs exist between algorithm correctness, speed, and resource usage?
- How does changing a model parameter affect convergence?
- Can I explain how attention mechanisms work mathematically in a Transformer?
II. Engineering Knowledge
Concept Card: Scalable Data Handling
Your ability to process large-scale data (TB-level) using distributed systems like Spark, and Hadoop. For example, you can build a data pipeline and processes logs from millions of users daily.
Concept Card: Deployment & Optimization
You must have the skills to deploy models in production and optimize for speed, size, and memory. For example, Converting PyTorch to others, using Tensor to hit 30 FPS on an edge device.
Concept Card: Model Acceleration Techniques
You must know the methods to pruning, quantization, distillation, and others to make models deployable in real-time environments.
Concept Card: Large Model Engineering
Prompt engineering, fine-tuning LLMs, using LangChain to build applications like fine-tuning GPT with domain-specific data, implementing a Retrieval-Augmented Generation (RAG) system.
Heuristic Questions
- How do you optimize inference time without sacrificing accuracy?
- What’s the cost of your model in terms of latency, compute, and memory?
- How do you detect and debug a production model failure?
III. Business Knowledge
Concept Card: Business
Like how to perform user retention.
Concept Card: KPI Modeling
Your knowledge about business metrics like CTR, GMV, ROI is essential.
Business Domain Knowledge
Preventing false positives and anti-fraud systems.
Heuristic Questions
- How does this model directly contribute to revenue or cost savings?
- If you improve one metric, what’s the possible negative impact on another?
- What offline metrics align most closely with your online KPIs?
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