About The Position
Liquid AI, spun out of MIT CSAIL, develops general-purpose AI systems that operate efficiently across various deployment targets, including data center accelerators and on-device hardware, prioritizing low latency, minimal memory usage, privacy, and reliability. They partner with enterprises in consumer electronics, automotive, life sciences, and financial services and are rapidly expanding. This specific opportunity involves building the company's internal operating system, focusing on data and agent infrastructure to enhance internal efficiency and scalability. This is a foundational role, requiring the individual to establish the unified company data graph and an agent layer from scratch, without existing teams or legacy systems. The core idea is to replace coordination overhead with visibility and guesswork with informed judgment.
Requirements
- Equally comfortable designing a data architecture, writing the integrations, deploying the infrastructure, and iterating on the agent layer
- Thinks in systems, seeing connections between various organizational information flows (e.g., Slack, Linear, GitHub, Rippling)
- Ships fast with high standards, capable of delivering the first version of the data graph in weeks and making pragmatic tradeoffs
- Understands LLMs deeply, with real experience in prompt engineering, tool use, evals, and practical limits of models
- Operates with high autonomy, thriving with direct access to leadership and broad latitude to make decisions
- 5+ years of software engineering with significant experience building data pipelines, integrations, or internal platforms
- Hands-on experience building with LLMs in production: agent systems, tool use, RAG, or similar
- Strong Python
- Comfortable with TypeScript for frontend/tooling as needed
- Experience integrating SaaS APIs (Slack, GitHub, Google Workspace, HRIS systems, or similar)
- Track record of shipping systems from zero to one with minimal guidance
Nice To Haves
- Experience with data modeling, knowledge graphs, or organizational analytics
Responsibilities
- Build the unified company data graph by integrating systems across execution (GitHub, Linear), communication (Slack, email, Zoom, calendars), model performance (W&B, eval dashboards), and operations (Rippling, Vanta, Ramp, Runway)
- Design and ship agents that surface performance signals, resource allocation suggestions, bottleneck detection, and opportunity visibility to leadership
- Start with observability, with the first milestone being a real-time map of work, ownership, and impact across the company
- Progress from visibility to recommendations to partial automation, following the progressive autonomy principle: never automate a decision you do not yet understand
- Own the entire stack: data pipelines, APIs, agent orchestration, evals, and the interfaces leadership uses to interact with the system
Benefits
- Competitive base salary with equity in a unicorn-stage company
- 100% of medical, dental, and vision premiums for employees and dependents
- 401(k) matching up to 4% of base pay
- Unlimited PTO plus company-wide Refill Days throughout the year
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
