Real-Time AI Research Scientist

adaption

United States

Vor Ort

USD 190.000 - 240.000

Vollzeit

vor 20 Stunden
Sei unter den ersten Bewerbenden

Eine vollständige Bewerbung in einer Minute

Maßgeschneiderter Lebenslauf und Anschreiben – versandbereit.

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Benefits dieser Stelle

Flexible work arrangements
Travel stipend
Lunch stipend
Medical benefits and PTO

Zusammenfassung

Adaption is seeking a PhD-qualified AI researcher to design and optimize intelligent systems that interact with real-world data in real time. You will co-design algorithms, work across software, hardware, and ML stacks, and push for measurable product impact.

You will partner with product teams to build efficient, scalable models and interfaces, focusing on data-efficient learning and practical deployment in dynamic environments.

Qualifikationen

  • A PhD or equivalent research experience in a computer science field.
  • 4-5+ years of industry experience in AI or ML research.
  • Deep expertise in model efficiency, real time alignment or algorithmic optimization.
  • Systems thinking to optimize across the ML stack.
  • Strong Python programming and experience with DL frameworks (PyTorch, JAX, TensorFlow).

Aufgaben

  • Innovation: co-design algorithms that react real-time to product signals and feedback.
  • Cross-Stack Optimization: collaborate across software, hardware, and algorithmic domains.
  • Measure impact: ensure real world value through experiments and product integration.

Kenntnisse

Python
Real-time learning
Model efficiency
Algorithmic optimization
Systems thinking
Industry lab experience

Ausbildung

PhD or equivalent CS research

Tools

PyTorch
JAX
TensorFlow
RLHF
Fine-tuning

Jobbeschreibung

Adaption is seeking a PhD-qualified AI researcher to design and optimize intelligent systems that interact with real-world data in real time. You will co-design algorithms, work across software, hardware, and ML stacks, and push for measurable product impact.

You will partner with product teams to build efficient, scalable models and interfaces, focusing on data-efficient learning and practical deployment in dynamic environments.

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