Research Engineer - Formal Methods/LLMs

Huawei; Markham; On-site; CAD 80,000 - 100,000; Full time

Job summary

Join a forward-thinking company as a Research Engineer, where you will work with the Intelligent Testing Technology Team at the Waterloo Research Centre. This role focuses on groundbreaking research in neuro-symbolic systems, integrating large language models with formal methods to enhance AI capabilities. Collaborate with industry leaders and contribute to innovative projects that shape the future of technology. Ideal candidates will have a Ph.D. in a relevant field and experience with symbolic computation tools. This is a unique opportunity to be part of a team at the forefront of AI advancements.

Qualifications

• Ph.D. in relevant fields with a focus on symbolic techniques.
• Experience in developing symbolic computation tools is essential.

Responsibilities

• Engage in research on neuro-symbolic systems to enhance LLM outputs.
• Collaborate on research publications to disseminate findings.

Skills

Neuro-symbolic systems

Symbolic AI

Formal methods

Programming language theory

Logic

Symbolic verification

Functional programming (Haskell)

Category Theory

Education

Ph.D. in Computer Science

Ph.D. in Software Engineering

Ph.D. in Mathematics

Tools

Model checkers

Theorem provers

Compilers

Job description

Huawei Canada has an immediate 12-month contractopening for a ResearchEngineer.

About the team:

The Intelligent Testing Technology Team, currently a part of the Waterloo Research Centre, is at the forefront of integrating large language models (LLMs) with formal methods to advance artificial intelligence. By harnessing LLMs' strengths in natural language processing and generation, this team explores their synergy with the precision of formal verification techniques. As part of this team, you will collaborate with industry leaders on groundbreaking projects and contribute to shaping the future of technology.

About the job:

• Engage in research projects focused on neuro-symbolic systems (NSSs) that leverage symbolic AI and formal methods to enhance and refine LLM outputs, ensuring the generation of acceptable symbolic results

• Implement innovative NSS designs and validate their intended properties

• Collaborate on research publications to disseminate findings

#LI-T1

About the ideal candidate:

• A Ph.D. in Computer Science, Software Engineering, Mathematics, or a related field, with a preference for candidates specializing in symbolic techniques, particularly in formal methods tools

• Proven experience in developing symbolic computation tools, such as programming language tools, compilers, model checkers, theorem provers, or similar applications. Familiarity with functional programming languages, especially Haskell, is an asset

• A solid understanding of programming language theory, logic, and symbolic verification is an asset

• A strong interest in the integration of LLMs with symbolic tools

• Knowledge of Category Theory is an asset