Sentra
Machine Learning Research Scientist
San Francisco / Bay Area
Sponsorship not specified$150k-$300kDetected 286 days ago
PythonDistributed SystemsAlgorithmsMachine LearningPyTorchNLPLLMsResearchCommunication
About the role
- As a Research Scientist, you will tackle fundamental problems in knowledge representation, temporal reasoning, and semantic compression.
Responsibilities
- Build LLM-powered information extraction pipelines that process unstructured communications and text data into structured entity-relationship representations.
- Develop memory consolidation algorithms that validate information through multiple observations, merge duplicate entities, and prune ephemeral data.
- Design temporal knowledge graph architectures that model organizational execution state as living, continuously updated systems rather than static records.
- Create graph attention mechanisms and reasoning systems for complex causal queries about blockers, dependencies, and outcome patterns.
- Design entity resolution systems handling identity evolution where entities merge, split, and transform through time.
- Relocation Support: Available for on-site hires
Requirements
- Deep knowledge of knowledge graphs, graph neural networks, or temporal reasoning demonstrated through shipped systems and architectural exploration.
- Track record of publishing research (conference papers, technical blog posts, or detailed technical documentation) and exploring novel architectures.
- Ability to move between theoretical investigation and practical implementation, shipping research into production.
Nice to have
- Proficiency in Python and modern ML frameworks (PyTorch preferred) with experience deploying models at scale.
Skills
- Latest MacBook Pro and AI development tools (ChatGPT Pro, Claude Pro, Cursor, etc.)
- Graph databases (Neo4j, TigerGraph, Neptune) and query optimization for large-scale graphs.
- Information theory, compression, or temporal data structures.
- Causal inference, probabilistic reasoning, or Bayesian methods.
- Distributed systems, stream processing, or real-time ML serving.
- Human memory and cognition models.
- Privacy-preserving ML (federated learning, differential privacy, secure multi-party computation).
- Enterprise AI systems, workflow automation, or organizational software.
- Publications at top-tier conferences (NeurIPS, ICML, ICLR, KDD, EMNLP, ACL, WWW, SOSP, OSDI).
Compensation
- $150,000 - $300,000
- Total estimated annual
Benefits
- ~$30K-$35K in addition to base and equity.
- Medical, dental, and vision
- $2,500/month to cover meals, transport, gym memberships, or other personal productivity needs
- Build meta-learning systems that identify organizational patterns and recognize when current situations match historical success or failure indicators.
- Develop privacy-preserving cross-organizational learning using federated learning and differential privacy techniques.
- 5+ years building novel systems in machine learning, NLP, knowledge graphs, or related areas with evidence through publications, production implementations, or significant open-source contributions.
- Equity: 0.3% - 2% depending on level
- Learning & Growth: Dedicated budget for conferences, courses, and professional development
- Flexible Time Off Policy
This listing is sourced directly from Sentra's careers page and normalized into a canonical job model.