Назад
4 дня назад

Applied Scientist Machine Learning Engineer Gaia (AI)

311 000 - 512 000$
Формат работы
hybrid
Тип работы
fulltime
Грейд
senior
Страна
UK/US
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Описание вакансии

TL;DR
Applied Scientist Machine Learning Engineer Gaia (AI) (Generative World Models and Autonomous Driving): Develop efficient generative world models and planners for closed-loop driving simulation with an accent on real-time rollouts, controllable scene editing, and sim-to-real evaluation. Focus on optimizing latent compression and inference latency, defining long-horizon fidelity metrics, integrating models into reinforcement-learning pipelines, and mentoring junior researchers.

Applied Scientist Machine Learning Engineer Gaia

Company

Wayve

Conditions

6 days agoSalary: 311K - 512K

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

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