The real jobYou're not just training models - you're building the infrastructure that lets us experiment with crazy ideas at 2 AM and deploy them by lunch. You'll work on everything from NLP models understanding recruiter-speak to recommendation systems that actually recommend good matches.

What you'll actually be doing:

• Designing neural architectures for problems that don't have Stack Overflow answers

• Building training pipelines that don't require a PhD to operate

• Creating custom loss functions that capture business metrics, not just mathematical ones

• Implementing papers that came out last week because they might give us an edge

• Debugging why the model performs worse after adding more data (yes, this happens)

• Building experiment tracking systems so we know what actually worked

• Making models that are explainable enough that regulators don't shut us down

You probably have:

• Implemented papers from scratch (and found errors in them)

• Built custom architectures for specific problems

• Experience with distributed training (and the pain it brings)

• Understanding of when not to use deep learning

• Ability to read PyTorch/TensorFlow source code when docs fail

• Projects showing creative applications of DL beyond standard problems

Epic work that gets our attention:

• Your implementation became the reference implementation

• You've found and fixed bugs in major DL frameworks

• Your blog post explaining a DL concept is the top Google result

• You've trained models on hardware held together with duct tape

• Your research got cited by the authors of the paper you were implementing

• You built a DL solution that replaced a team of 10 people

Why people fail:They think more layers solve everything. Or they can't explain why batch norm works. Or they've never deployed a model that had to run efficiently.