Defense AI Engineer

required

Deep learning (CS231n + fast.ai)

CNNs, transformers, training dynamics.

2 resources

required

Reinforcement learning basics

Q-learning, policy gradients, PPO. The math + practical training tricks.

1 resources

required

PyTorch to senior-IC fluency

Custom datasets, distributed training, mixed precision, profiler.

2 resources

required

Mission planning + multi-agent coord

How autonomy is described in mission terms — go-to-waypoint, ISR loiter, contested overflight.

0 resources

recommended

Anduril Lattice architecture (public docs)

The reference defense-AI platform — what's public is enough to learn the shape.

1 resources

recommended

Palantir Foundry + AIP basics

The other dominant defense data + AI platform.

1 resources

optional

Shield AI Hivemind + V-BAT

Smaller, focused stack on autonomy for ISR + strike platforms.

1 resources

required

Model quantization + pruning

Get a model from a 7B-param prototype to something that runs on a Jetson.

1 resources

required

TensorRT + ONNX Runtime

NVIDIA's production inference path.

1 resources

optional

Federated learning concepts

Train across distributed nodes without centralizing data. Increasingly relevant for classified domains.

1 resources

required

DoD Responsible AI Strategy

The policy framework every defense AI engineer is expected to know.

1 resources

recommended

JADC2 + CJADC2 overview

Joint All-Domain Command and Control — the umbrella program for defense data integration.

1 resources