ML Systems Engineer

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 Systems Engineer, you’ll be embedded alongside our ML researchers to build and maintain the low-level systems, tools, and infrastructure that power our models. From debugging training failures to optimising experiment runtimes, you’ll be the backbone of our research operations – enabling rapid iteration, robust scaling, and deep introspection of symbolic reasoning models.

This is a hands-on engineering role for someone who thrives at the intersection of systems and ML, enjoys building tools for others, and wants to accelerate cutting-edge AI research.

Your Focus

• Build, scale, and maintain high-performance training pipelines for symbolic ML models (JAX, PyTorch, etc).
• Optimise runtime, memory usage, and debugging capabilities across GPU-backed compute clusters.
• Develop tooling and automation for model inspection, verification, and evaluation – with a focus on researcher ergonomics.
• Collaborate closely with ML researchers to translate experimental needs into scalable infrastructure.
• Own model reproducibility: manage versioning, config tracking, and experiment logging.
• Help diagnose correctness and performance issues in training, inference, and deployment workflows.
• Contribute to model-serving and deployment infra for internal demos and external applications.

About You

• Strong software engineering background with experience in ML infrastructure, distributed systems, or high-performance computing.
• Proven ability to write performant, maintainable code in Python and JAX or PyTorch.
• Familiarity with containerisation and orchestration (Docker, Kubernetes).
• Experience with profiling, debugging, and optimising ML training pipelines at scale.
• A systems mindset — you enjoy reducing complexity, spotting bottlenecks, and making things faster and more reliable.
• Collaborative and curious — you want to work with researchers and engineers to push the boundaries of what’s possible.

What We Offer

Competitive compensation, including an early-stage startup equity package. Salary and equity levels are aligned with your experience and the scope of impact.

📍 This is an onsite role based in our London office (66 City Rd).

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.