AI Engineer
TechnologyYou build the AI systems that everyone's afraid of and nobody fully understands. You'll train models, wrangle datasets, and spend most of your time explaining to executives that AI can't do what they promised investors. The hype cycle is exhausting, the compute costs are astronomical, and your GPU is always out of memory.
Salary Range
Low
$120k
Median
$185k
High
$300k
10-Year Growth
much faster
US Workers
45K
Education
Master's or PhD in CS/ML (or equivalent experience with published work)
Environment
remote
Tools & Technical Skills
- ▸Python (PyTorch, TensorFlow, Hugging Face Transformers)
- ▸Large language model fine-tuning and prompting
- ▸RAG pipeline design (vector databases, embeddings)
- ▸MLOps and model deployment (MLflow, SageMaker, Vertex AI)
- ▸GPU computing and distributed training
- ▸Data preprocessing and feature engineering
- ▸REST API and microservice integration
People & Mindset Skills
- ▸Rapid learning of new techniques
- ▸Critical thinking about AI limitations
- ▸Collaboration with product teams
- ▸Communication of technical concepts
- ▸Ethical reasoning
Learn the skills
Courses and certifications to get you job-ready
Python (PyTorch, TensorFlow, Hugging Face Transformers)
RAG pipeline design (vector databases, embeddings)
REST API and microservice integration
What you'll actually do
- 01Train a model for 72 hours only to discover your training data was garbage
- 02Explain to the CEO that AI can't actually do what they promised the board
- 03Fine-tune large language models and pretend you fully understand why they hallucinate
- 04Debug pipeline failures that turn out to be a missing comma in a config file
- 05Monitor model performance and discover it's slowly getting worse and nobody noticed
- 06Write evaluation benchmarks that prove your model works great on paper but terrifies users in production
- 07Fight for GPU budget in a world where a single training run costs more than a new car
Related Shifts
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