
Symbolica is an AI research lab pioneering the application of category theory to enable logical reasoning in machines. We’re a well-resourced, nimble team of experts on a mission to bridge the gap between theoretical mathematics and cutting-edge AI, creating symbolic reasoning models that think like humans – precise, logical, and interpretable. While others focus on scaling data-hungry neural networks, we’re building AI that understands the structures of thought, not just patterns in data. Our approach combines rigorous research with fast-paced, results-driven execution. We’re reimagining the very foundations of intelligence while simultaneously developing product-focused machine learning models in a tight feedback loop, where research fuels application. Our vision is to create AI systems that transform industries, empowering machines to solve humanity’s most complex challenges with precision and insight.

Symbolica is an AI research lab pioneering the application of category theory to enable logical reasoning in machines. We’re a well-resourced, nimble team of experts on a mission to bridge the gap between theoretical mathematics and cutting-edge AI, creating symbolic reasoning models that think like humans – precise, logical, and interpretable. While others focus on scaling data-hungry neural networks, we’re building AI that understands the structures of thought, not just patterns in data. Our approach combines rigorous research with fast-paced, results-driven execution. We’re reimagining the very foundations of intelligence while simultaneously developing product-focused machine learning models in a tight feedback loop, where research fuels application. Our vision is to create AI systems that transform industries, empowering machines to solve humanity’s most complex challenges with precision and insight.
Founded: 2022
Headquarters: San Francisco (also lists London presence)
Team size: ~20 employees
Core focus: Applying category theory & type theory to symbolic reasoning and agentic systems
Notable product: Agentica — type-safe, composable framework for agent workloads
Funding signal: Series A (Apr 2024) led by Khosla Ventures; total reported raise $33M
Bridging mathematical foundations (category theory, type theory) with practical AI to enable symbolic reasoning and formal semantics in agentic systems.
2022
Software Development
33000000
Series A announced April 2024; participating investors reported to include Abstract, Buckley Ventures, Day One Ventures, and General Catalyst.
“Series A led by Khosla Ventures with participation from Abstract, Buckley Ventures, Day One Ventures, and General Catalyst”
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About Us
Symbolica is an AI research lab pioneering the application of category theory to enable logical reasoning in machines.
We’re a well-resourced, nimble team of experts on a mission to bridge the gap between theoretical mathematics and cutting-edge technologies, creating symbolic reasoning models that think like humans – precise, logical, and interpretable. While others focus on scaling data-hungry neural networks, we’re building AI that understands the structures of thought , not just patterns in data.
Our approach combines rigorous research with fast-paced, results-driven execution. We’re reimagining the very foundations of intelligence while simultaneously developing product-focused machine learning models in a tight feedback loop, where research fuels application.
Founded in 2022, we’ve raised over $30M from leading Silicon Valley investors, including Khosla Ventures, General Catalyst, Abstract Ventures, and Day One Ventures, to push the boundaries of applying formal mathematics and logic to machine learning.
Our vision is to create AI systems that transform industries, empowering machines to solve humanity’s most complex challenges with precision and insight. Join us to redefine the future of AI by turning groundbreaking ideas into reality.
About The Role
As a DevOps Engineering Lead you will lead the design, build, and optimize the infrastructure and tools that enable us to take our research and development efforts from the lab into a highly reliable, performant and secure software stack in production. You'll help accelerate the processes involved in going from research prototypes into production and enterprise ready platforms with security, availability and reliability in mind.
Your work will be at the intersection of research and engineering, ensuring our R&D team has the robust platform they need to push the boundaries of AI, working with our GPU vendors, cloud providers, and on-prem servers.
📍 This is an onsite role that is based in our SF office (345 California St.)
Key Responsibilities
Focus on improving the reliability and performance of our Lambda cluster and model training pipeline.
Assist in managing multiple Kubernetes environments across cloud providers
Maintain and build the internal observability platform across all environments, covering everything from GPUs, AI applications and distributed backend systems.
Take ownership of our model training and deployment systems, bringing them to a more scalable, production-ready state.
Aid in building comprehensive CI tests for GitOps repositories and promotion systems
Build and maintain different environments for research and client facing products according to best practices
About You
5+ years of experience in DevOps, or infrastructure roles, with at least 2 years in machine learning infrastructure or MLOps. It would be a benefit if you have either built, maintained, or managed ML infrastructure using DevOps practices in the past.
Proficient in cloud-native architectures, with the ability to make the right tradeoffs where necessary
Experienced with Linux, containers, GPU management, Nix, Kubernetes and an interest in making sure the infrastructure behind our models is secure by design.
Exceptional problem-solving skills with the ability to nimbly solve edge-cases with minimum disruption.
Solid software engineering skills in Rust, Golang or Python
What We Offer
Read More About Symbolica
Symbolica is an equal opportunities employer. We celebrate diversity and are committed to creating an inclusive environment for all employees, regardless of race, gender, age, religion, disability, or sexual orientation.