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- Tasks: Design and optimise GPU kernels for symbolic reasoning models using CUDA.
- Company: Join Symbolica, an innovative AI research lab transforming machine learning with category theory.
- Benefits: Enjoy competitive pay, equity options, and a supportive, diverse work environment.
- Why this job: Be part of a pioneering team redefining AI, blending rigorous research with practical applications.
- Qualifications: Strong programming skills in C/C++/Rust, GPU programming experience, and familiarity with compiler optimisations required.
- Other info: Onsite role in London; visa sponsorship available for qualified candidates.
The predicted salary is between 43200 - 72000 £ per year.
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.
About the role
As a Founding GPU & Compiler Software Engineer at Symbolica, you will specialize in the design, development, and optimization of GPU kernels and algorithms to support the training and inference of symbolic reasoning models. You will leverage frameworks like CUDA and CUTLASS, along with compiler optimization techniques, to push the boundaries of performance for high-dimensional computation.
Your focus:
- Developing and optimizing GPU kernels for high-performance symbolic reasoning and numerical algorithms using CUDA.
- Designing and implementing domain-specific compiler optimizations for GPU acceleration, ensuring efficient transformation and execution of symbolic computation workloads.
- Collaborating with mathematicians and researchers to design highly efficient implementations of complex algorithms.
- Analyzing and optimizing GPU performance, focusing on memory management, thread utilization, compiler-generated optimizations, and computation throughput.
- Building and maintaining scalable, reusable GPU-accelerated libraries tailored for symbolic reasoning workloads.
- Profiling and benchmarking kernel performance, identifying compiler inefficiencies, and implementing solutions for maximum efficiency.
About you
- Strong proficiency in at least one high-performance programming language (C, C++, Rust, Haskell, or Julia) and familiarity with Python.
- Proficiency in GPU programming with CUDA, including experience with kernel development, compiler optimizations, and performance tuning.
- In-depth knowledge of GPU architecture, including memory hierarchies, thread blocks, warps, and scheduling.
- Experience with compiler development, LLVM/MLIR, or domain-specific language (DSL) optimizations.
- Familiarity with tensor operations and matrix multiplications is a plus.
- Proven optimizing numerical algorithms for high-performance computing environments.
- Familiarity with LSP (Language Server Protocol) and a background in linear algebra, symbolic computation, or related mathematical fields are strong pluses.
We offer competitive compensation, including an attractive equity package, with salary and equity levels aligned to your experience and expertise. This is an onsite role based in our London office (66 City Rd). We are able to sponsor a Skilled Worker visa for qualified candidates applying to this position. This specific role exceeds the minimum salary threshold set by the UK government for Skilled Worker visa sponsorship. Please note that English language proficiency at B2 level or higher is required for this role. 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.
Founding GPU & Compiler Software Engineer employer: Symbolica
Contact Detail:
Symbolica Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Founding GPU & Compiler Software Engineer
✨Tip Number 1
Make sure to showcase your experience with GPU programming and CUDA in any discussions or interviews. Highlight specific projects where you've optimised GPU kernels or developed high-performance algorithms, as this will demonstrate your hands-on expertise.
✨Tip Number 2
Familiarise yourself with Symbolica's mission and the unique approach they take towards AI and mathematics. Being able to discuss how your skills align with their vision will set you apart and show your genuine interest in the role.
✨Tip Number 3
Network with professionals in the field of GPU programming and compiler development. Engaging with communities or attending relevant meetups can provide insights and connections that may help you during the application process.
✨Tip Number 4
Prepare to discuss your understanding of compiler optimisations and performance tuning in detail. Be ready to explain complex concepts clearly, as collaboration with mathematicians and researchers will be a key part of the role.
We think you need these skills to ace Founding GPU & Compiler Software Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with GPU programming, CUDA, and any relevant compiler optimisations. Use specific examples that demonstrate your proficiency in high-performance programming languages and your understanding of GPU architecture.
Craft a Compelling Cover Letter: In your cover letter, express your passion for AI and how your skills align with Symbolica's mission. Mention your experience in developing GPU kernels and optimising algorithms, and explain why you are excited about the opportunity to work at an innovative AI research lab.
Showcase Relevant Projects: If you have worked on projects involving symbolic reasoning, GPU acceleration, or compiler development, be sure to include these in your application. Provide links to any code repositories or publications that can showcase your expertise.
Highlight Collaboration Skills: Since the role involves collaborating with mathematicians and researchers, emphasise your teamwork and communication skills. Share examples of how you've successfully worked in interdisciplinary teams to achieve project goals.
How to prepare for a job interview at Symbolica
✨Showcase Your Technical Skills
Be prepared to discuss your proficiency in high-performance programming languages like C, C++, or Rust. Highlight any relevant projects where you've developed GPU kernels or optimised algorithms, as this will demonstrate your hands-on experience.
✨Understand Symbolic Reasoning
Familiarise yourself with the concepts of symbolic reasoning and how they differ from traditional machine learning approaches. Being able to articulate how your skills can contribute to Symbolica's mission will set you apart.
✨Prepare for Problem-Solving Questions
Expect technical questions that assess your problem-solving abilities, particularly in GPU programming and compiler optimisations. Practice explaining your thought process clearly, as communication is key in collaborative environments.
✨Research the Company Culture
Symbolica values diversity and inclusion, so be ready to discuss how you can contribute to a positive team environment. Understanding their vision and demonstrating alignment with their values will show your genuine interest in the role.
