META AI & SYSTEMS ENGINEERING
Skills to Put on Resume for Machine Learning Engineer at
Meta
PyTorch, Recommendation Systems (RecSys), Large Scale LLMs, Distributed Training & C++ Optimization
Meta MLE Hiring Focus:
Meta evaluates ML Engineers on end-to-end ownership—from custom deep learning model
design (PyTorch/Distributed) and high-throughput RecSys/Ranking architectures to low-latency C++/CUDA serving at
billion-user scale.
1. TECHNICAL COMPETENCIES & AI SYSTEMS ENGINEERING
Core Frameworks & ML Programming
Frameworks:
PyTorch (PyTorch 2.x,
TorchScript, TorchDynamo, TorchRec), Caffe2,
TensorFlow.
High-Performance Languages:
Python
(Async, C-Extensions), C++ (C++17/20),
CUDA, Hack/PHP, Java, SQL.
Mathematical Foundations:
Linear Algebra,
Multivariable Calculus, Probability & Statistics,
Convex Optimization.
Algorithm & Data Structures:
Graph
Algorithms, Dynamic Programming, High-
Throughput Tree Ensembles.
RecSys, LLMs & Generative AI
Recommendation Systems (RecSys):
Personalization, Collaborative Filtering, Two-
Tower Models, Deep & Cross Networks (DCN),
Retrieval & Ranking.
Large Language Models (LLMs):
Llama 3/4
architectures, Transformer Fine-Tuning (LoRA,
QLoRA), SFT, RLHF, DPO.
Computer Vision & Speech:
Diffusion Models,
Object Detection, Multimodal Transformers,
Self-Supervised Learning.
NLP & Embeddings:
Vector Search,
Approximate Nearest Neighbor (FAISS), Dense/
Sparse Representation.
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Machine Learning Engineer at Meta Resume Skills Blueprint
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