Real-Time AI Systems Research Engineer

adaption; Ciudad de México; Presencial; MXN 900.000 - 1.300.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Flexible work: In-person collaboration

Adaption Passport: travel stipend

Lunch Stipend

Well-Being: medical benefits and PTO

Descripción de la vacante

Adaption is seeking a research-focused role that aims to create real-world impact through efficient intelligence, real-time learning, and adaptive interfaces. You will help design systems that can interact with the world, steer synthetic data effectively, and optimize across the ML stack for real-time performance.

The role emphasizes building frontier innovations that improve efficiency and enable scalable, real-time adaptation across product signals and feedback loops.

Formación

• PhD or equivalent research experience in computer science.
• 4-5+ years of industry experience.
• Deep expertise in at least one area: model efficiency, real time alignment or algorithmic optimization.
• Strong programming in Python and experience with deep learning frameworks (PyTorch, JAX, TensorFlow).
• Experience in an industry lab with computing at scale.

Responsabilidades

• Innovation: innovate on our product to co-design algorithms that react real-time to product signal and feedback.
• Cross-Stack Optimization: collaborate across software, hardware, and algorithmic domains to achieve system-wide efficiency gains.
• Measure what matters: ensure real world impact and that algorithms interact effectively with the world.

Conocimientos

Python

Deep learning

PyTorch

JAX

TensorFlow

RLHF

Finetuning

Systems thinking

Real-time learning

Industry lab experience

Educación

PhD or equivalent research experience in Computer Science

Descripción del empleo

Adaption is seeking a research-focused role that aims to create real-world impact through efficient intelligence, real-time learning, and adaptive interfaces. You will help design systems that can interact with the world, steer synthetic data effectively, and optimize across the ML stack for real-time performance.

The role emphasizes building frontier innovations that improve efficiency and enable scalable, real-time adaptation across product signals and feedback loops.