Principal Machine Learning Engineer GAIA

Responsibilities

• Lead Gaia’s post-training and closed-loop pipeline, including fine-tuning, alignment, experimentation, and targeted data curation.
• Improve long-horizon autoregressive generation so Gaia produces stable, coherent, and steerable rollouts.
• Contribute to model architecture and training-strategy decisions across pre-training, post-training, and application layers.
• Translate post-training improvements into measurable downstream impact with research, applications, simulation engineering, and cloud/infrastructure teams.
• Provide technical leadership through mentorship, reviews, and setting high engineering and research standards.
• Improve inference optimization, deployment readiness, reliability, and performance under relevant constraints.

Requirements

• Hands-on experience post-training or fine-tuning large-scale language, video, or other foundation models.
• Experience with world models, autoregressive generation, and long-horizon generation.
• Experience with diffusion or flow models and an understanding of 3D vision.
• Strong understanding of model architecture and ability to contribute to architecture and training decisions.
• Strong hands-on engineering skills with modern ML stacks, including PyTorch, debugging, and performance- and reliability-minded development.
• Typically 5+ years of relevant industry experience.
• Advanced degrees are valued, but substantial applied experience is important.
• Experience with inference optimization, large-model deployment under latency or compute constraints, data or training pipelines, distributed training, efficiency, reliability, and technical leadership is desirable.

Benefits

• Full-time position based in London, UK, with a hybrid working arrangement combining office/workshop and home working.
• Core working hours with flexibility to determine a schedule that works for the individual and team.
• Inclusive interview experience with accommodations or adjustments available on request.