Residency: Build Real-Time Adaptive AI Systems

adaption; Netherlands; On-site; EUR 18,000 - 32,000; Full time

Job summary

adaption invites you to join an 8-month modelling residency in the Netherlands, embedded in a frontier lab. You will work on real production and research projects and be mentored by the modelling team to accelerate your skills.

You'll explore adaptive algorithms, real-time learning, and interfaces while collaborating across software, hardware, and ML domains to push practical efficiency and impactful outcomes.

Qualifications

• A degree or equivalent research/engineering experience in a computer science field.
• Interest in model efficiency, synthetic data, interfaces, real-time alignment, or algorithmic optimization.
• Systems thinking — ability to understand and optimize across the full ML stack.
• Strong Python skills and experience with deep learning frameworks (PyTorch, JAX, or TensorFlow).
• Familiarity with model optimization techniques such as RLHF and fine-tuning.
• Strong communication and self-awareness — collaborate in a remote environment and welcome feedback.
• Care about technical excellence and last mile impact; value owning outcomes end to end.

Responsibilities

• Innovation: Build the future of adaptive algorithms that continuously learn and react to feedback.
• Cross-Stack Optimization: Collaborate across software, hardware, and ML domains to drive system efficiency.
• Measure What Matters: Focus on real-world impact and product signal in guiding work.

Skills

Python

Deep learning

Remote collaboration

Communication

Adaptability

Systems thinking

Education

Computer science degree

Tools

PyTorch

JAX

TensorFlow

Job description

adaption invites you to join an 8-month modelling residency in the Netherlands, embedded in a frontier lab. You will work on real production and research projects and be mentored by the modelling team to accelerate your skills.

You'll explore adaptive algorithms, real-time learning, and interfaces while collaborating across software, hardware, and ML domains to push practical efficiency and impactful outcomes.