What are the responsibilities and job description for the ML Compiler Software Engineer position at Symbolica AI?
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
At Symbolica, we’re building the next frontier of symbolic reasoning and architecture specification in AI. As our Founding ML Compiler Engineer, you'll lead the development of the compiler stack and GPU kernels behind our in-house, dependently typed language — a new way to specify and run AI architectures that are correct by construction. This role is a chance to help invent the programming language of the next AI paradigm.
📍 This is an onsite role based in our SF office.
Your Focus
- Translate high-level symbolic architecture specs (written in our custom dependently typed DSL) into efficient compute graphs and GPU-executable code.
- Build and optimize GPU kernels using CUDA and Rust, targeting training and inference of symbolic AI models.
- Design and implement compiler infrastructure (e.g. custom IRs, graph lowering, scheduling, memory planning) using MLIR, LLVM, or your own abstractions.
- Collaborate with mathematicians and researchers to co-design the system from first principles, ensuring semantic correctness throughout.
- Profile and debug across the stack — from type-level constructs to kernel performance — ensuring mathematical expressiveness meets real-world throughput.
About You
- Strong experience with Rust or other performant system languages (e.g. C , Haskell, Julia).
- Expertise in compilers, intermediate representations, and building static analyses or program transformations.
- Familiarity with dependent types, symbolic computation, or strongly typed DSLs.
- Experience with CUDA, GPU kernels, and performance tuning at the memory/threading level.
- (Nice to have) Background in functional programming, category theory, or type theory.
What We Offer
- Competitive salary and early-stage equity package.
- A high-trust, execution-first culture with minimal bureaucracy.
- Direct ownership of meaningful projects with real business impact.
- A rare opportunity to sit at the interface between deep research and real-world productisation.
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.
