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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates strong proficiency in C++ and Python, with a deep understanding of systems programming fundamentals and experience in GPU programming using CUDA. Capable of conducting performance benchmarking and optimization in distributed AI frameworks and large-scale cluster environments.
Highest-signal resume keywords
C++ ProgrammingPython ProgrammingGPU Programming with CUDADistributed AI FrameworksPerformance Optimization
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Systems Programming FundamentalsMemory ManagementConcurrencyOS InternalsPerformance BenchmarkingLarge-Scale Cluster OrchestrationTelemetry Tools
Tools & Technologies
Linux Development EnvironmentPyTorchJAXDeepSpeedVLLMSGLangDynamo/TRT-LLM
Certifications & Qualifications
Bachelor's Degree in Computer EngineeringBachelor's Degree in Computer ScienceMaster's Degree in Computer EngineeringMaster's Degree in Computer Science
Industry Keywords
AI InfrastructurePerformance BottlenecksDistributed TrainingInference WorkloadsHybrid Work Environment
Tech Stack
Tools & technologiesLinuxNode.jsPythonPyTorchC++
About the role
Key responsibilities & impact- Help redefine the infrastructure layer of AI
- Bridge high-level AI frameworks and low-level system software
- Design and implement communication and execution primitives for running large-scale AI models efficiently across thousands of GPUs
- Collaborate across the stack to influence the design of foundational technology for next-generation AI models
- Identify and resolve performance bottlenecks in distributed training and inference workloads through deep-dive software-hardware interface analysis
- Conduct rigorous performance benchmarking and characterization on multi-node clusters
Requirements
What you’ll need- Strong proficiency in C++ and Python
- Deep understanding of systems programming fundamentals, including memory management, concurrency, and OS internals
- Proficiency in a Linux development environment
- Experience with GPU programming using CUDA and performance optimization for parallel architectures
- Familiarity with distributed AI frameworks such as PyTorch, JAX, or DeepSpeed and/or inference engines such as vLLM, SGLang, or Dynamo/TRT-LLM
- Hands-on experience with large-scale cluster orchestration and telemetry tools
- Bachelor's or Master's degree in Computer Engineering, Computer Science, or a related field
- Authorization to work in Canada is requested in the application
- Ability to work in a hybrid role based in Montreal
Benefits
Comp & perks- Meaningful equity
- Benefits
- RRSP