---
title: 'Machine Learning Research Scientist at Sentra'
canonical: 'https://feeny.ai/job/machine-learning-research-scientist-sentra-san-francisco-8z6rf1eprfga'
type: 'job'
last_seen: '2026-09-08'
---

# Machine Learning Research Scientist at Sentra

- **Company:** Sentra
- **Location:** San Francisco, CA / Bay Area
- **Compensation:** $120k–$300k
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2025-10-09
- **Last confirmed live:** 2026-09-08
- **Apply:** https://jobs.ashbyhq.com/sentra/03976429-eb79-48df-8439-29f0cdaba859

## Job description

## POSITION OVERVIEW

Sentra is building organizational superintelligence through memory infrastructure that reasons across time, causality, and context. As a Research Scientist, you will tackle fundamental problems in knowledge representation, temporal reasoning, and semantic compression. You will design and implement systems that maintain execution state for entire organizations, consolidate millions of micro-events into durable knowledge, and learn patterns that predict events before it happens.

## KEY 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.
- Research lossy semantic compression using information-theoretic principles to condense event streams into query-relevant long-term memory.
- Design entity resolution systems handling identity evolution where entities merge, split, and transform through time.
- 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.
- Publish research findings and contribute to the broader research community on knowledge graphs and organizational intelligence.

## MUST-HAVE REQUIREMENTS

- 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.
- Deep knowledge of knowledge graphs, graph neural networks, or temporal reasoning demonstrated through shipped systems and architectural exploration.
- Strong ML and NLP foundation, particularly in information extraction, entity resolution, or semantic representation.
- Proficiency in Python and modern ML frameworks (PyTorch preferred) with experience deploying models at scale.
- 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.

Bonus skills:

- 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 AND BENEFITS

- Base Salary: $150,000 – $300,000
- Equity: 0.3% - 2% depending on level
- Comprehensive Health Coverage: Medical, dental, and vision
- Wellness & Productivity Stipend: $2,500/month to cover meals, transport, gym memberships, or other personal productivity needs
- Hardware & Tools: Latest MacBook Pro and AI development tools (ChatGPT Pro, Claude Pro, Cursor, etc.)
- Learning & Growth: Dedicated budget for conferences, courses, and professional development
- Relocation Support: Available for on-site hires
- Flexible Time Off Policy

Total estimated annual benefits package: ~$30K–$35K in addition to base and equity.

## About Sentra

## Company Overview
- **One-liner**: Sentra builds an AI-powered organizational memory layer that captures and structures every interaction, decision, and commitment across a company’s tools, making them queryable by both humans and agents.
- **Entity Type**: Private (Seed-stage)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2025
- **Founders**: Ashwin Gopinath (CEO), Andrey Starenky (CTO), Al Rey (CRO)

## Core Business
- **Primary industries**: Enterprise SaaS, AI Infrastructure, Productivity
- **Target customers**: B2B – teams and enterprises that want a unified knowledge base spanning meetings, messages, documents, tickets, and CRM
- **Mission**: “Enterprise General Intelligence starts with memory” – creating a shared organizational memory that enables intelligent coordination across the entire company

## Products & Services
- **[Sentra](https://www.sentra.app/)**: A cloud or self-hosted platform that ingests data from 200+ tools (meetings, chat, email, code, CRM, etc.) and builds a bi-temporal context graph. Users query the graph in plain English; agents access it via REST or MCP. Key features include factual, action, and interaction memory layers, confidence-scored identity resolution, and automated tracking of commitments and drift. It is SOC 2 Type II and ISO 27001 certified and can be deployed in an isolated VPC or fully air-gapped on-premises.

## Market Standing
- **Valuation/Market Cap**: Not disclosed
- **Total Funding**: $5.0 million (Seed round, January 2026)
- **Notable Investors/Partners**: Led by a16z speedrun and Together Fund, with participation from Parable, Shaad Khan, Precursor Ventures, and others. [a16z speedrun](https://speedrun.a16z.com/companies/sentra) | [LinkedIn](https://www.linkedin.com/company/sentra-app)
- **Growth Signals**: Team of ~12–16 employees with presence in the US, Canada, and Japan. Founders and early engineers hail from MIT, Caltech, Google, Amazon, Meta, Microsoft, Apple, and Databricks. Sentra claims state-of-the-art performance on the MEME benchmark, scoring 40% on Cascade (field average 3%) and 43% on Absence (field average 1%).

## Competitive Advantages
- **Bi-temporal context graph**: Facts carry when they became true and when they stopped, enabling reasoning about change rather than simple retrieval.
- **No model training on customer data**: Data privacy is a core design principle; the platform does not use customer data to train AI models.
- **Self-hosted or air-gapped deployment**: Suitable for enterprises with strict data residency or compliance requirements.
- **Deep integration ecosystem**: Plugs into 200+ tools via native connectors and standard protocols (REST, MCP).

## Strategic Focus
- Become the standard memory layer for enterprises, enabling both human teams and AI agents to act on a single source of organizational truth.
- Continue expanding integrations and improving graph-based reasoning over raw language data.
- Compete with RAG-based memory systems by offering superior accuracy and temporal reasoning.

## Why Work Here
- **Culture**: Deeply technical, research-driven environment led by multiple-time founders with academic and big-tech backgrounds.
- **Team size**: Small (12–16 people) – opportunity for high impact and ownership.
- **Investor backing**: Backed by top-tier firms (a16z speedrun, Together Fund).
- **Work policy**: Headquarters in San Francisco; distributed team across the US, Canada, and Japan (exact remote/hybrid policy not publicly specified).
- **Notable perks**: Focus on cutting-edge AI research; direct work on agentic memory infrastructure that is being cited in academic benchmarks.

## Sources
1. [sentra.app](https://www.sentra.app/)
2. [sentra.app/about](https://www.sentra.app/about)
3. [linkedin.com/company/sentra-app](https://www.linkedin.com/company/sentra-app)
4. [jobs.ashbyhq.com/sentra](https://jobs.ashbyhq.com/sentra)
5. [speedrun.a16z.com/companies/sentra](https://speedrun.a16z.com/companies/sentra)

## Other roles at Sentra

- [Founding Product Designer](https://feeny.ai/job/founding-product-designer-sentra-san-francisco-3vt3ap581mzn) — San Francisco, CA / Bay Area
- [Senior Backend Software Engineer](https://feeny.ai/job/senior-backend-software-engineer-sentra-san-francisco-g5hjb4e1jtwp) — San Francisco, CA / Bay Area
- [Machine Learning Research Scientist](https://feeny.ai/job/machine-learning-research-scientist-autoscience-san-mateo-wf3axmr9k87g) — San Mateo