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 Machine Learning Infrastructure Engineering Lead, you will design, build, and optimize the infrastructure and tools that enable our research and development efforts. You'll lead the development of scalable infrastructure that powers our machine learning experiments, model training, and deployment. 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.
Key responsibilities
- Leading the implementation and management of infrastructure for large-scale machine learning workflows, including training systems and model deployment.
- Developing tools and frameworks to support the global team’s experiments and ensure reproducibility and scalability.
- Optimizing compute resources and ensure efficient use of cloud and on-premises hardware for training and inference.
- Building and maintaining CI/CD pipelines tailored for machine learning development.
- Collaborating closely with machine learning scientists, researchers and engineers to identify and address infrastructure needs.
About you
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
- 5+ years of experience in software engineering or infrastructure roles, with at least 2 years in machine learning infrastructure.
- Proficiency in cloud platforms (e.g., AWS, Lambda) and containerization tools (e.g., Docker, Kubernetes).
- Experience building CI/CD pipelines for machine learning workflows.
- Exceptional problem-solving skills, with the ability to design and implement robust, scalable systems.
This is an onsite role based in our London office.
We offer competitive compensation, including an attractive equity package, with salary and equity levels aligned to your experience and expertise.
Read more about Symbolica:
- https://fortune.com/2024/04/09/vinod-khosla-former-tesla-autopilot-engineer-ai-models/
- https://venturebeat.com/ai/move-over-deep-learning-symbolicas-structured-approach-could-transform-ai/
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.
Top Skills
What We Do
Symbolica is an AI research lab pioneering the application of category theory to enable logical reasoning in machines. Our mission is to bridge the gap between theoretical mathematics and cutting-edge AI, creating powerful 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.
Sounds exciting? – Join us to redefine the very foundations of intelligence.
