Senior Research Scientist: Real-Time Learning Innovator

adaption; San Francisco (CA); Vor Ort; USD 180.000 - 300.000; Vollzeit

Benefits dieser Stelle

Flexible Bay Area collaboration

Global-first, distributed team

Lunch stipend

Well-being: medical benefits and PTO

Adaption Passport travel stipend

Zusammenfassung

adaption in San Francisco seeks a research scientist to build models that evolve with the world. Frontiers include efficiency, gradient-free exploration, real-time learning, and thoughtful interface design.

You will collaborate across software, hardware, and algorithms to achieve system-wide improvements, focusing on real-world impact and scalable compute. If you care about intelligent, efficient systems, we'd love to hear from you.

Qualifikationen

• PhD or equivalent research experience in computer science.
• 4–5+ years of industry experience.
• Publications in ICML, NeurIPS, ACL, ICLR, EMNLP.
• Deep expertise in model efficiency, real-time alignment, or algorithmic optimization.
• Systems thinking across the ML stack.
• Strong Python programming.
• Experience with PyTorch, JAX, TensorFlow.
• Knowledge of RLHF and fine-tuning techniques.
• Experience in an industry lab with computing at scale.

Aufgaben

• Innovation: You have ideas and you move fast. You care about impact, not just effort.
• Cross-Stack Optimization: You collaborate across software, hardware, and algorithmic domains to unlock system-wide efficiency gains.
• Measure What Matters: Real-world impact is the ultimate signal of value; product thinking is inseparable from research.

Kenntnisse

Research experience

Publications

Python programming

Systems thinking

Adaptability

Ausbildung

PhD in CS or related field

Tools

PyTorch

JAX

TensorFlow

Jobbeschreibung

adaption in San Francisco seeks a research scientist to build models that evolve with the world. Frontiers include efficiency, gradient-free exploration, real-time learning, and thoughtful interface design.

You will collaborate across software, hardware, and algorithms to achieve system-wide improvements, focusing on real-world impact and scalable compute. If you care about intelligent, efficient systems, we'd love to hear from you.