• 3+ years building deep learning systems in industry or research, with shipped models or published work to show for it.
• Deep hands-on experience with at least one of: LLMs, VLMs, image or video generative models (architecture, training and inference). For the World Models track: diffusion models, autoregressive transformers, latent-variable models or video prediction.
• You have actually trained models yourself: pre-training runs, distributed infrastructure, the works. Connecting APIs, building agents or a quick fine-tune is not enough.
• Experience with deep learning infrastructure: streaming datasets, checkpointing and state management, distributed training strategies, debugging numerics and training instabilities.
• Strong Python and PyTorch or JAX. You can profile, optimize data loaders and write maintainable research code.
• Strong coding fundamentals. The process includes a LeetCode-style coding round, so come prepared.
• Empirical rigor: careful evaluations, honest baselines, clear experiment docs, crisp communication of trade-offs.
• You are proactive: you can take an open, messy problem and drive it without waiting for a spec.
• Working English.
• Robotics or autonomous driving experience; familiarity with simulators (Isaac Sim, MuJoCo) and sim-to-real.
• Experience applying RL to LLMs or robotics.
• Experience with VLA models; familiarity with OpenVLA, Physical Intelligence (π) models or similar open-source frameworks.
• Experience with world models for robotics or autonomous driving (action-conditioned video models, learned simulators, model-based RL).
• Proven productization of deep nets: latency and throughput constraints, on-device optimization, fast inference for generative models.
• Publications at top-tier DL conferences (NeurIPS, ICML, ICLR, CoRL, CVPR) or equivalent open-source contributions.