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Target Roles

Role → Skills Mapping

Netflix Research Scientist 5/6 (\(466K–\)750K)

Requirement Wiki Section
LLM development, post-training, fine-tuning, distillation 1. Post-Training
Distributed training 2. Distributed Training
Python, PyTorch, TensorFlow Throughout
Spark, Hive, Hadoop 4. Data at Scale
Reinforcement learning 7. RL for LLMs
Personalization, recommender systems 5. Recommender Systems
Production-ready systems 8. MLOps
Conversational agents, search, NLP 6. Transformers

Other Target Roles

Company Role Comp Range Key Differentiator
Anthropic Research Engineer \(300K–\)500K RLHF, safety, constitutional AI
OpenAI Research Scientist \(300K–\)600K Post-training, reasoning, evals
Google DeepMind Research Scientist \(250K–\)500K Gemini, multimodal, RL
Meta FAIR Research Engineer \(250K–\)450K Llama, open-source, systems
Spotify ML Engineer \(200K–\)350K Personalization, recsys, Spark
Apple ML Engineer \(250K–\)400K On-device ML, efficiency

Study Priority by Role Type

"I want to work on LLM post-training" (Netflix, Anthropic, OpenAI)

  1. Post-Training (SFT, DPO, GRPO) ⭐⭐⭐
  2. RL for LLMs ⭐⭐⭐
  3. Distributed Training ⭐⭐⭐
  4. Transformers ⭐⭐
  5. Inference & Serving ⭐⭐

"I want to work on recommendations/personalization" (Netflix, Spotify)

  1. Recommender Systems ⭐⭐⭐
  2. Data at Scale (Spark/Hive) ⭐⭐⭐
  3. Transformers ⭐⭐
  4. Post-Training ⭐⭐
  5. MLOps ⭐⭐

"I want to work on ML infrastructure" (Netflix, Google, Meta)

  1. Distributed Training ⭐⭐⭐
  2. Inference & Serving ⭐⭐⭐
  3. MLOps ⭐⭐⭐
  4. Data at Scale ⭐⭐
  5. Post-Training ⭐⭐