Higgsfield ML position prep kitv1

How do we design runs so any teammate can rerun them and meaningfully compare outcomes?

What principles turn messy web data into a lawful, safe corpus that streams efficiently to GPUs?

How do we fit large vision/video models within memory budgets and report metrics that truly reflect sample quality?

Why do tensor shapes and linear maps determine how computations and derivatives flow in deep networks?

How do eigendecompositions and Jacobians reveal where and how signals amplify or vanish during learning?

How does variational inference turn uncertainty into a tractable objective, with Gaussians and reparameterisation making gradients flow?

How does index notation unify attention mechanics and performance-aware tensor layout?

How do we turn a correct baseline into an efficient, idiomatic system while choosing losses that align with the task?

What makes a training loop reliable enough for CI yet debuggable when things go wrong on the GPU?

What truly governs training throughput on modern GPUs, and how can we systematically surface and remove the bottleneck?

How do we make models that no single GPU can hold trainable, and what evidence shows our parallelization strategy is efficient and correct?

How do we maximise concurrent inference on a fixed GPU while preserving model quality and avoiding out-of-memory failures?

How do architecture choices and training discipline turn images into compact codes that learn stably and reconstruct faithfully?

What practices keep deep convolutional models learning steadily and prove that the improvements we see are real?

Which structural choices let generators capture both fine detail and global context, and how do we verify the latent space is meaningful?

Why does learning the score of a noised distribution enable effective denoising, and how do time and noise schedules shape that capability?

How does a continuous-time diffusion process translate into the discrete denoising updates used in practical DDPM implementations?

What tradeoffs separate U‑Net and transformer backbones, and how do latent normalization and representation choices shape training stability?

Why does a pre‑norm transformer with efficient attention and adaptive modulation make diffusion denoisers both stable and scalable?

How do we turn text conditioning into a training pipeline we can trust at web scale?

How do loss parameterisations and conditioning strategies connect to the ways we measure prompt adherence and optimize efficiency?

How do advanced parameterisations and rigorous evaluation help us debug diffusion training and build evidence for changes that matter?

How do we turn a trained model into a fast, reliable product without sacrificing quality?

How can we steer the sampler toward the prompt while keeping the VAE decode crisp and faithful to the latent?

How do capacity choices and positional signals together determine what a diffusion transformer can model?

How does the latent representation and loss design shape the realism and sharpness a decoder can produce?

How can we add structure or style controls and adapt a pretrained diffuser by training only small side branches?

How do we capture dependencies across space and time while keeping attention computation tractable?

How do we quantify and then preserve temporal coherence so generated video reads as one continuous, believable motion?