Full-Time
Senior/Staff Full-Stack Product Engineer
Confirmed live in the last 24 hours
LlamaIndex
11-50 employees
Connects data sources to large language models
No salary listed
Senior
San Francisco, CA, USA
Hybrid-friendly culture based out of the downtown San Francisco office.
- 3+ years of experience
- Experience with shipped web applications or completely new product features from scratch. Have gone from prototype to production serving end users in a short amount of time.
- Shipped AI/LLM-native applications - from UX to core algorithms.
- Iterated rapidly with customers - translate user feedback into features back to proactive user discovery. Help jointly develop and iterate on product roadmap for core features.
- Worked with product and design, demonstrating instances of wearing the PM hat yourself to proactively scope and design features.
- Shipped applications that scaled to millions of users.
- [Primary] Front-end/full-stack work for our managed applications/SaaS offering.
- [Secondary] General feature development, maintenance, and education for our open-source frameworks (Python and Typescript).
- [Secondary] Work closely with prospects and customers to build POC and production solutions.
- Nice to have: machine learning and NLP experience.
LlamaIndex.ai provides a data framework that enables businesses to connect their custom data sources to large language models (LLMs), which are AI systems capable of understanding and generating human-like text. The framework supports various types of data, including structured data from sources like Excel and SQL, semi-structured data from APIs such as Slack and Salesforce, and unstructured data like web pages and images. This versatility allows businesses of all sizes to gain insights from their data. Operating on a business-to-business (B2B) model, LlamaIndex.ai likely uses a subscription-based revenue model, offering clients ongoing access to its services. The company's goal is to help businesses leverage their data effectively to make informed, data-driven decisions.
Company Size
11-50
Company Stage
Series A
Total Funding
$26.7M
Headquarters
San Francisco, California
Founded
2023
Simplify's Take
What believers are saying
- LlamaIndex's involvement in AGNTCY boosts its AI agent interoperability leadership.
- The $19M Series A funding supports team expansion and product development for LlamaCloud.
- Partnership with NVIDIA enhances AI solutions and market reach for enterprise automation.
What critics are saying
- Competition from Microsoft's AutoGen framework challenges LlamaIndex's market position.
- NVIDIA's Nemotron models may attract LlamaIndex's potential customers.
- AGNTCY's industry standards may require costly adaptations for LlamaIndex.
What makes LlamaIndex unique
- LlamaIndex offers a unique data framework connecting custom data sources to LLMs.
- It supports structured, semi-structured, and unstructured data integration for diverse business needs.
- LlamaIndex's Agent Document Workflow enhances AI agents' decision-making in complex processes.
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Benefits
Health Insurance
Dental Insurance
Vision Insurance
Unlimited Paid Time Off
Company Equity
Meal Benefits
Growth & Insights and Company News
6 month growth
↓ -2%1 year growth
↑ 7%2 year growth
↑ 62%Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More. One goal for an agentic future is for AI agents from different organizations to freely and seamlessly talk to one another. But getting to that point requires interoperability, and these agents may have been built with different LLMs, data frameworks and code.To achieve interoperability, developers of these agents must agree on how they can communicate with each other. This is a challenging task. A group of companies, including Cisco, LangChain, LlamaIndex, Galileo and Glean, have now created AGNTCY, an open-source collective with the goal of creating an industry-standard agent interoperability language. AGNTCY aims to make it easy for any AI agent to communicate and exchange data with another.Uniting AI Agents“Just like when the cloud and the internet came about and accelerated applications and all social interactions at a global scale, we want to build the Internet of Agents that accelerate all of human work at a global scale,” said Vijoy Pandey, head of Outshift by Cisco, Cisco’s incubation arm, in an interview with VentureBeat. Pandey likened AGNTCY to the advent of the Transmission Control Protocol/Internet Protocol (TCP/IP) and the domain name system (DNS), which helped organize the internet and allowed for interconnections between different computer systems. “The way we are thinking about this problem is that the original internet allowed for humans and servers and web farms to all come together,” he said
LlamaIndex is a simple, flexible framework for building knowledge assistants using LLMs connected to your enterprise data.
LlamaIndex, a startup founded in 2023 by former Uber researchers Jerry Liu and Simon Suo, has raised $19 million in a Series A funding round led by Norwest Venture Partners, with participation from Greylock. This brings their total funding to $27.5 million. The company focuses on developing custom agents using unstructured data. The funds will be used to expand their 20-member team and enhance product development, particularly for their enterprise service, LlamaCloud.
In part to help fund LlamaCloud's development, LlamaIndex recently raised $19 million in a Series A funding round that was led by Norwest Venture Partners, and saw participation from Greylock as well.
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More. Microsoft has updated its AutoGen orchestration framework so the agents it helps build can become more flexible and give organizations more control. AutoGen v0.4 brings robustness to AI agents and solves issues customers identified around architectural constraints. “The initial release of AutoGen generated widespread interest in agentic technologies,” Microsoft researchers said in a blog post. “At the same time, users struggled with architectural constraints, an inefficient API compounded by rapid growth and limited debugging and intervention functionality.” The researchers added that customers are asking for stronger observability and control, flexibility around multi-agent collaboration and reusable components. AutoGen v0.4 is more modular and extensible, with scalability and distributed agent networks. It adds asynchronous messaging; cross-language support, observability and debugging; and built-in and community extensions. Asynchronous messaging means agents built with AutoGen v0.4 support event-driven and request-interaction patterns. The framework is more modular, so developers can add plug-in components and build long-running agents. It also enables users to design more complex and distributed agent networks. AutoGen’s extension module simplifies the process of working with multi-agent teams and advanced model clients