Senior Applied Scientist, Personalization & Memory Systems

Socket.dev; Seattle (WA); On-site; USD 167,000 - 226,000; Full time

Job summary

Socket.dev is seeking a Senior Applied Scientist to lead the research direction for persistent, compounding memory in AI systems. You will own the scientific roadmap, run a research agenda with deployment targets, and mentor junior scientists in a forming team.

You will define the knowledge acquisition and retrieval roadmap, lead research on how systems learn from experience, and drive end-to-end research from problem framing to production impact.

Qualifications

• 4+ years of applied research experience.
• 3+ years of building machine learning models for business applications.
• PhD, or Master’s degree with 6+ years of applied research experience.
• Experience programming in Java, C++, Python or related languages.
• Experience with neural deep learning methods and machine learning.

Responsibilities

• Define the scientific roadmap for personalization initiatives and knowledge extraction at scale.
• Lead end-to-end research from problem formulation through production impact measurement.
• Mentor Applied Scientists and establish scientific standards for the team.
• Partner with engineering leadership to translate research into architecture decisions shaping the product.
• Drive model architecture, training methodology, and evaluation frameworks balancing rigor and business impact.
• Publish at top-tier venues and advance the state of the art in applied knowledge systems.

Skills

Applied research

Machine learning

Education

PhD

Master's + 6+ years applied research

Tools

Java

C++

Python

TensorFlow

PyTorch

Job description

Socket.dev is seeking a Senior Applied Scientist to lead the research direction for persistent, compounding memory in AI systems. You will own the scientific roadmap, run a research agenda with deployment targets, and mentor junior scientists in a forming team.

You will define the knowledge acquisition and retrieval roadmap, lead research on how systems learn from experience, and drive end-to-end research from problem framing to production impact.