Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
AI @ 3
LLM
Machine Learning @ 3
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
The Foundations Research team works on high-risk, high-reward ideas that could shape the next decade of AI, advancing the science and data behind training and scaling future frontier models. The Search team builds agentic search by co-designing model-system interfaces with serving, indexing, and retrieval systems to translate model intent into reliable real-world actions.
Responsibilities
- Develop embedding models and retrieval systems optimized for grounding, relevance, and adaptive reasoning.
- Collaborate with researchers and engineers to build end-to-end infrastructure for training, evaluating, and integrating embeddings into frontier models.
- Drive innovation in dense, sparse, and hybrid representation techniques, metric learning, and learning-to-retrieve systems.
- Work closely with Pretraining, Inference, and other Research teams to integrate retrieval throughout the model lifecycle.
- Contribute to AI systems with memory and knowledge-access capabilities based on learned representations.
- Design new embedding training objectives, scalable vector store architectures, and dynamic indexing methods.
Requirements
- Proven experience leading high-performance teams of researchers or engineers in machine learning infrastructure or foundational research.
- Deep technical expertise in representation learning, embedding models, or vector retrieval systems.
- Familiarity with transformer-based large language models and how embedding spaces interact with language model objectives.
- Research experience in contrastive learning, supervised or unsupervised embedding learning, or metric learning.
- A track record of building or scaling large machine learning systems, particularly embedding pipelines in production or research contexts.
- A first-principles mindset for challenging assumptions about retrieval and memory for large models.
Work Model
This role is based in San Francisco, California. The team uses a hybrid work model requiring three days in the office per week.
Benefits
- Equity, performance-related bonuses for eligible employees, and a salary range of $445,000–$555,000 per year.
- Medical, dental, and vision insurance, with employer contributions to Health Savings Accounts.
- Pre-tax accounts for health, dependent care, and commuter expenses.
- 401(k) retirement plan with employer match.
- Paid parental, medical, caregiver, and sick or safe leave.
- Flexible paid time off for exempt employees and up to 15 paid days annually for non-exempt employees.
- Paid company holidays and coordinated office closures.
- Mental health and wellness support.
- Employer-paid basic life and disability coverage.
- Annual learning and development stipend.
- Daily office meals and eligible meal delivery credits.
- Relocation support for eligible employees.
- Additional benefits may include charitable donation matching and wellness stipends.
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