Research Engineer / Research Scientist - Foundations Retrieval IC
Develops embedding models and retrieval systems to enable frontier AI models to access relevant information dynamically. Requires deep expertise in representation learning, vector retrieval, and transformer LLMs, with experience scaling ML systems.
About the job
Responsibilities
- Tackle embedding models and retrieval systems optimized for grounding, relevance, and adaptive reasoning.
- Collaborate with a team of researchers and engineers building 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.
- Collaborate closely with Pretraining, Inference, and other Research teams to integrate retrieval throughout the model lifecycle.
- Contribute to OpenAI’s long-term vision of AI systems with memory and knowledge access capabilities rooted in learned representations.
Requirements
- Proven experience leading high-performance teams of researchers or engineers in ML infrastructure or foundational research.
- Deep technical expertise in representation learning, embedding models, or vector retrieval systems.
- Familiarity with transformer-based LLMs and how embedding spaces can interact with language model objectives.
- Research experience in areas such as 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 how retrieval and memory should work for large models.
Skills
Embeddings, Retrieval Systems, Representation Learning, Transformer-Based Llms, Contrastive Learning, Metric Learning, Vector Stores, Machine Learning Infrastructure, Dense Representations, Sparse Representations
Similar jobs
AI Research jobs
OpenAI
San Francisco, CA
Researcher or engineer focused on designing, evaluating, and productionizing oversight systems and safety mitigations for autonomous AI agents. The role requires strong systems or security reasoning, threat-modeling ability, and experience building practical evaluations and controls.
OpenAI
San Francisco, CA
Researcher focused on training and evaluating frontier AI agents, mining incidents, and building scalable safety measurement systems. The role requires strong research or ML engineering execution, quantitative judgment, and the ability to own ambiguous projects end to end.
Anthropic
San Francisco, CA
Research Engineer building large-scale AI capability evaluations, telemetry, data pipelines, and analysis tools for Anthropic’s Takeoff Intel team. The role requires hands-on large language model experimentation, rapid prototyping, data expertise, and strong research collaboration.
Thinking Machines Lab
San Francisco, CA
Research role focused on improving agentic coding capabilities through reinforcement-learning training, synthetic data, coding environments, reward design, and evaluations. Requires strong Python engineering, scalable distributed-training experience, and a bachelor’s degree or equivalent; research experience and a PhD are preferred.
Thinking Machines Lab
San Francisco, CA
Conduct AI safety research across data curation, post-training, evaluations, synthetic data, and red-teaming to improve model reliability on harmful and dual-use requests. The role requires AI safety experience, Python, deep learning frameworks, and scalable technical research skills.