Real-Time AI Research Scientist — Real-World Impact

adaption; Ciudad de México; On-site; MXN 600,000 - 1,200,000; Full time

Benefits offered by this job

Flexible work with global team

Annual travel stipend

Lunch stipend

Comprehensive medical benefits

Job summary

adaption is seeking a research professional focused on real-world impact, efficiency, and learning systems. The role emphasizes designing algorithms that interact with the world, evaluating real-time data, and improving products through intelligent feedback mechanisms.

The candidate will work across software, hardware, and ML stacks, contributing to gradient-free exploration, synthetic data strategies, and interface design for practical deployment in a global, fast-moving context.

Qualifications

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

Responsibilities

• Innovation: innovate on our product to co-design algorithms that react real-time to product signal and feedback. Design new ways of giving feedback that drive better algorithms.
• Cross-Stack Optimization: collaborate across software, hardware, and algorithmic domains to achieve system-wide efficiency gains
• Measure what matters: we believe strongly that the ultimate signal of value is whether we have real world impact; our current algorithms interact with the world.

Skills

PhD in CS or equivalent research field

4-5+ years industry experience

Model efficiency

Real-time alignment

Algorithmic optimization

Systems thinking

Python programming

Deep learning frameworks

Education

PhD or equivalent research experience in computer science

Tools

PyTorch

JAX

TensorFlow

Job description

adaption is seeking a research professional focused on real-world impact, efficiency, and learning systems. The role emphasizes designing algorithms that interact with the world, evaluating real-time data, and improving products through intelligent feedback mechanisms.

The candidate will work across software, hardware, and ML stacks, contributing to gradient-free exploration, synthetic data strategies, and interface design for practical deployment in a global, fast-moving context.