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Adaption Labs

AI research startup pursuing continual adaptation

Modelling Resident

Applied Machine LearningConfirmed live in the last 24 hours
ContractNo salary listedRemote

About the Job

Requirements

  • A degree or equivalent research or engineering experience in a computer science field.
  • At least one project touching model efficiency, synthetic data, interfaces, real-time alignment, or algorithmic optimization.
  • The ability to understand and optimize across the full machine learning stack.
  • Strong Python skills and experience with deep learning frameworks such as PyTorch, JAX, or TensorFlow.
  • Familiarity with model optimization techniques such as reinforcement learning from human feedback and fine-tuning.
  • The ability to collaborate in a remote environment and remain open to feedback.

Responsibilities

  • Build adaptive algorithms that continuously learn.
  • Co-design algorithms that react in real time to product signals and feedback, and explore new ways of capturing feedback to improve those algorithms.
  • Collaborate across software, hardware, and algorithmic domains to drive system-wide efficiency gains.
  • Measure algorithm value through real-world product impact.

About the Company

Adaption Labs

AI research startup pursuing continual adaptation

Adaption Labs builds AI systems that learn from real-world interactions instead of relying on ever-larger language models. Their products aim for continual, real-time adaptation so AI can improve while being cost-efficient and secure, without full retraining. The company works toward flexible, personalized, and accessible AI that can adapt to specific enterprise workflows, reducing the need for massive data centers and helping more organizations participate in AI development. Unlike models that depend mainly on scaling compute, Adaption Labs focuses on efficient learning from experience to provide practical, enterprise-ready AI that evolves over time.

Company size
11-50
Company stage
Seed
Total funding
$50M
Headquarters
San Francisco, CA
Founded
2025

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