Impact-Driven Real-Time AI Research Scientist

adaption; Netherlands; On-site; EUR 90,000 - 150,000; Full time

Benefits offered by this job

Flexible in-person collaboration (Bay-

Travel stipend: Adaption Passport

Meal stipend

Medical benefits & vacation

Job summary

Adaption is seeking a research-focused role aimed at real-world impact, advancing efficiency, gradient-free exploration, real-time learning, and interface design. We envision a world where synthetic data is cheap, and the data space is malleable to drive meaningful UI and algorithm performance.

If you resonate with building intelligent systems that interact with the world and push the frontiers of continual adaptation, we want to hear from you.

Qualifications

• PhD or equivalent research experience in a computer science field.
• 4-5+ years of industry experience.
• Deep expertise in at least one area: model efficiency, real time alignment or algorithmic optimization.
• Systems thinking ability to understand and optimize across the full ML stack.
• Strong programming skills in Python. Experience with deep learning frameworks (PyTorch, JAX, TensorFlow).
• Knowledge of model optimization techniques (RLHF, finetuning).
• 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 are capable of interacting with the world which is why product matters so much.

Skills

Python programming

Deep learning frameworks

Model optimization

Systems thinking

Real-time learning

Education

PhD or equivalent

Tools

PyTorch

JAX

TensorFlow

Job description

Adaption is seeking a research-focused role aimed at real-world impact, advancing efficiency, gradient-free exploration, real-time learning, and interface design. We envision a world where synthetic data is cheap, and the data space is malleable to drive meaningful UI and algorithm performance.

If you resonate with building intelligent systems that interact with the world and push the frontiers of continual adaptation, we want to hear from you.