Real-Time AI Research Scientist | Impact-Driven Innovation

adaption; Singapore; Presencial; SGD 120.000 - 180.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Flexible work

Travel stipend

Lunch stipend

Well-Being benefits

Descripción de la vacante

adaption in Singapore seeks a research role focused on real-world impact, building frontier innovation around efficiency, gradient-free exploration, real-time learning and interface design. A revolution is underway where synthetic data is cheap enough to treat the data space as malleable, optimizing for desirable properties and making previously invisible worlds visible.

The most intelligent systems require agents who care how a model interacts with the world, with emphasis on cross-disciplinary

Formación

• PhD or equivalent research experience in computer science.
• 4–5+ years of industry experience.
• Deep expertise in model efficiency, real-time alignment, or algorithmic optimization.
• Systems thinking across the full ML stack.
• Strong Python programming and DL frameworks (PyTorch, JAX, TensorFlow).
• Experience in an industry lab with computing at scale.

Responsabilidades

• Innovate to co-design algorithms that react real-time to product signal and feedback; design new feedback methods to improve algorithms.
• Cross-Stack Optimization: collaborate across software, hardware, and algorithms to achieve system-wide efficiency gains.
• Measure what matters: ensure real-world impact of deployed algorithms and products.

Conocimientos

Python programming

Real-time / streaming systems

Model efficiency

Algorithmic optimization

Systems thinking

Educación

PhD or equivalent research experience in computer science

Herramientas

PyTorch

JAX

TensorFlow

Descripción del empleo

adaption in Singapore seeks a research role focused on real-world impact, building frontier innovation around efficiency, gradient-free exploration, real-time learning and interface design. A revolution is underway where synthetic data is cheap enough to treat the data space as malleable, optimizing for desirable properties and making previously invisible worlds visible.

The most intelligent systems require agents who care how a model interacts with the world, with emphasis on cross-disciplinary