Overview In this role you will define and own the scientific roadmap for a new, memory-enabled personalization system. You will lead end-to-end research from problem formulation to production impact, guiding a growing team of scientists and translating ambiguous challenges into concrete ML solutions. You'll shape architecture decisions with engineers and publish advancements in the field. This is a high-impact, hands-on leadership position at the frontier of knowledge systems and AI memory.
Compensation / Benefits- health insurance (medical, dental, vision)
- 401(k) matching
- paid time off
- RSUs and sign-on payments
- par Ens: parental leave
- flexible spending accounts
Responsibilities- Define the scientific roadmap for knowledge acquisition, representation, and retrieval at scale
- Lead research on how AI systems should learn from experience-what to capture, how to generalize, when to forget
- Design evaluation frameworks for systems with novel quality metrics (knowledge, context, confidence)
- Own end-to-end research from problem formulation to production impact
- Mentor Applied Scientists and set scientific standards for a new team
- Collaborate with engineering leadership to translate research into architecture decisions
- Drive technical decisions on model architecture, training methodology, and evaluation frameworks balancing rigor with business impact
- Publish at top-tier venues and advance applied knowledge systems
Key requirements- 3+ years of building machine learning models for business applications
- PhD, or Master's degree and 6+ years of applied research experience
- Proficiency in programming languages such as Java, C++, Python or related
- Experience with neural deep learning methods and machine learning
- strong communication and stakeholder management
- mentorship and people leadership
- collaboration across scientists, engineers, and product managers
- neural deep learning
- machine learning
- problem formulation and evaluation design
