About Liquid AI

Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there.

The Opportunity

We are building the operating system of the company itself. Not the product we sell, but the internal data and agent infrastructure that makes Liquid run at the speed of a 10-person team while scaling well past that. The thesis is simple: replace coordination overhead with visibility, and replace guesswork with informed judgment. You will build the unified company data graph and the agent layer on top of it. This is a founding role. There is no existing team, no legacy system, no playbook.

What We're Looking For

We need someone who:

• Builds end-to-end:

You are equally comfortable designing a data architecture, writing the integrations, deploying the infrastructure, and iterating on the agent layer. This is not a role where you hand off specs to someone else.

• Thinks in systems:

You see the connections between a Slack message, a Linear ticket, a GitHub PR, and a Rippling org chart. You can model how information flows through an organization and where the gaps are.

• Ships fast with high standards:

We need the first version of the data graph running in weeks, not months. You know how to make pragmatic tradeoffs without building something you will regret.

• Understands LLMs deeply:

You will build agents on top of foundation models. You need real experience with prompt engineering, tool use, evals, and the practical limits of what models can and cannot do today.

• Operates with high autonomy:

You will have direct access to the leadership team and broad latitude to make decisions. We need someone who thrives with that, not someone who needs a product spec before writing code.

The Work

• 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. The first milestone is 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

Desired Experience

• 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

• Bonus: experience with data modeling, knowledge graphs, or organizational analytics

What We Offer

• Compensation:

Competitive base salary with equity in a unicorn-stage company

• Health:

We pay 100% of medical, dental, and vision premiums for employees and dependents

• Financial:

401(k) matching up to 4% of base pay

• Time Off:

Unlimited PTO plus company-wide Refill Days throughout the year