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 cycleBuild deployment infrastructure that works across cloud and on-prem environmentsCreate intuitive interfaces that make complex ML capabilities accessibleStrike the perfect balance between speed and maintainabilityMake technical decisions that scale from POC to production without rebuilding,
Required Capabilities
Proven track record of building and scaling systems from ground upDeep understanding of modern software architecture and best practicesExperience deploying ML systems in production environmentsStrong opinions about engineering practices, backed by real-world experienceAbility to identify when to build custom solutions vs. leverage existing tools,
Core Responsibilities
Architect and implement full-stack solutions for both internal and external usersDesign and build scalable backend services that support ML model deploymentCreate responsive frontends that make complex capabilities accessibleEstablish development workflows that enhance team productivityBuild deployment solutions that work seamlessly across diverse environmentsCollaborate with Product and ML teams to rapidly iterate on features,
The Right Candidate
Values pragmatic solutions over theoretical perfectionUnderstands that perfect is the enemy of done, but done wrong is the enemy of scalePrefers building systems from scratch over maintaining existing onesIs opinionated about architecture but flexible about implementationGets energized by creating order from chaos,
What You'll Gain
Greenfield opportunity to design and build critical systemsDirect collaboration with exceptional ML and Product teamsInfluence over core architectural decisionsChance to shape how enterprises deploy efficient AI modelsIdeal 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.