Our goal at Liquid is to build the most capable AI systems to solve problems at every scale, such that users can build, access, and control their AI solutions. This is to ensure that AI will get meaningfully, reliably and efficiently integrated at all enterprises. Long term, Liquid will create and deploy frontier-AI-powered solutions that are available to everyone.
Our ML technology is proven and validated. Now comes the engineering challenge: building the systems and infrastructure that turn theoretical capabilities into deployable products. This isn't about maintaining existing systems – it's about architecting the foundation for rapid ML development and deployment at scale.
The Role: Full-Stack Software Engineer
We're looking for engineers who understand that good infrastructure is the difference between theoretical and practical ML. The challenge isn't just writing code – it's making the right technical decisions that enable speed without sacrificing stability.
The Core Question
"Can you build systems that enable both rapid development and robust deployment of ML models, while resisting the urge to over-engineer?" If you have strong opinions about architecture but know when to be pragmatic, you might be who we're looking for.
What Success Looks Like
Design and implement internal systems that accelerate our ML development cycle
Build deployment infrastructure that works across cloud and on-prem environments
Create intuitive interfaces that make complex ML capabilities accessible
Strike the perfect balance between speed and maintainability
Make technical decisions that scale from POC to production without rebuilding
Required Capabilities
Proven track record of building and scaling systems from ground up
Deep understanding of modern software architecture and best practices
Experience deploying ML systems in production environments
Strong opinions about engineering practices, backed by real-world experience
Ability to identify when to build custom solutions vs. leverage existing tools
Core Responsibilities
Architect and implement full-stack solutions for both internal and external users
Design and build scalable backend services that support ML model deployment
Create responsive frontends that make complex capabilities accessible
Establish development workflows that enhance team productivity
Build deployment solutions that work seamlessly across diverse environments
Collaborate with Product and ML teams to rapidly iterate on features
The Right Candidate
Values pragmatic solutions over theoretical perfection
Understands that perfect is the enemy of done, but done wrong is the enemy of scale
Prefers building systems from scratch over maintaining existing ones
Is opinionated about architecture but flexible about implementation
Gets energized by creating order from chaos
What You'll Gain
Greenfield opportunity to design and build critical systems
Direct collaboration with exceptional ML and Product teams
Influence over core architectural decisions
Chance to shape how enterprises deploy efficient AI models
Ideal for engineers who've built and scaled systems from zero to production, understand the trade-offs at each stage of growth, and want to apply that knowledge to revolutionize how ML models are developed and deployed.