Applied Scientist Machine Learning Engineer Gaia

Skills

About the Role

You will develop efficient generative world models and planners for closed-loop driving simulation. You will optimize inference performance, define fidelity and coherence metrics, run experiments, integrate models into training and evaluation, measure sim-to-real performance, and mentor junior researchers.

Requirements

• 4+ years of machine learning research or engineering experience focused on generative video or world models
• Knowledge of diffusion and latent-video models
• Experience improving sampling efficiency or model throughput
• Experience with high-dimensional temporal or spatiotemporal data
• Python and PyTorch engineering skills
• Experience building research-grade production tools
• Publication record or contributions to open-source machine-learning tooling

Responsibilities

• Develop efficient generative world models with real-time rollouts and controllable scene editing
• Architect interactive world models for reinforcement learning, planning, and safety evaluation
• Optimize latent compression, context pruning, and inference latency
• Define metrics for long-horizon coherence, physics fidelity, and planner integration
• Run ablation and scaling studies
• Integrate models into closed-loop training and evaluation
• Measure sim-to-real gaps against on-road driving-model results
• Mentor junior researchers and shape technical roadmaps

Benefits

• Hybrid working
• Core working hours with schedule flexibility