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Research Scientist

Adaption· Modelling
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RemoteFull-timePosted 5mo ago

About the role

A research role focused on real-world impact, emphasizing efficiency, gradient-free exploration, real-time learning, and interface design. Works on leveraging synthetic data, real-time interaction, and cross-stack optimization to create adaptable AI systems.

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.

Qualifications

  • A 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

Skills

Required:PythonDeep learning frameworks

Benefits

  • Flexible work: In-person collaboration in the Bay Area, a distributed global-first team, and team offsites.
  • Adaption Passport: Annual travel stipend to explore a country you've never visited.
  • Lunch Stipend: Weekly meal allowance for take-out or grocery delivery.
  • Well-Being: Comprehensive medical benefits and generous paid time off.
Last updated Oct 15, 2026. Interested? Apply on the company site.
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About Adaption

Everything intelligent adapts. So should AI.

Adaptive DataAdaptive IntelligenceAdaptive InterfacesproductAI that adaptsAI that learnson-the-fly malleable datasetscontinual learning
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