InfoTasks Productivity Agents, DeepMind
InfoTasks Productivity Agents, DeepMind
InfoTasks Productivity Agents, DeepMind
InfoTasks Productivity Agents, DeepMind
DeepMind
Pharma, Medizintechnik
Zürich
Art der Beschäftigung: Vollzeit
70.500 CHF – 96.000 CHF (von XING geschätzt)Vor OrtZu den Ersten gehörenInfoTasks Productivity Agents, DeepMind
Über diesen Job
InfoTasks Productivity Agents, DeepMind
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Minimum qualifications:
- Experience building systems powered by LLMs or other ML models.
- Experience in software development in training, developing or applying machine learning models.
Preferred qualifications:
- Strong AI development knowledge, training GenAI models.
- Strong software engineering fundamentals, with a track record of shipping production-quality code.
- Strong collaboration and communication skills across research, engineering and product teams.
- Interest in AI agents and how they can make people more productive.
- Comfortable working in an ambiguous, changing research and product environment.
About the job
Artificial intelligence will be one of humanity’s most transformative inventions. At DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.
Responsibilities
- Build and optimize multi-agent hierarchical pipelines and cross-application reasoning agents for productivity agents.
- Develop, scale, and maintain automated evaluation raters to assess model capabilities under complex, real-world conditions.
- Maintain secure execution environments (Python Workspace API harnesses, Bash/gws CLI) to evaluate agent trajectories hermetically, and allow for large-scale distillation and training in the environments.
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