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 (Senior) Machine Learning Research Engineer, you will play a crucial role at the intersection of theoretical research and practical application. You’ll collaborate with world-class researchers to develop innovative symbolic reasoning models inspired by abstract mathematics and implement them at scale. This is an opportunity to work on some of the most challenging problems in machine reasoning while contributing to both foundational research and the engineering of real-world systems.
Your focus
- Conducting research into symbolic and categorical reasoning models, bridging abstract mathematics with machine learning.
- Translating complex theoretical insights into scalable, efficient coding implementations.
- Developing and optimizing machine learning pipelines for structured reasoning tasks, with a focus on interpretability and performance.
- Building robust experimentation platforms for large-scale training and evaluation of models.
- Collaborating with researchers to explore novel architectures and methodologies in logical reasoning and structured data.
- Benchmarking, debugging, and refining models to ensure reliability in real-world applications.
- Staying at the forefront of advancements in mathematics, machine learning, and AI research to inspire new approaches.
About you
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (PhD is a plus).
- Strong theoretical background in abstract mathematics, particularly category theory, type theory, or symbolic reasoning.
- Expertise in machine learning model development and optimization, with experience in structured data or reasoning tasks.
- Proficiency in at least one functional programming language (e.g., Haskell, Scala) or extensive experience with Python for deep learning applications.
- Solid software engineering skills, including performance optimization, version control, and CI/CD pipelines.
- Experience deploying machine learning models at scale and in production environments.
- Passion for exploring the intersection of mathematics and AI, and a collaborative mindset for working with researchers and engineers.
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
Symbolica AI London, England Office
London, United Kingdom
