Canva
Computer Software
MachineLearningEngineer(TrainingOptimization)
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“Machine Learning Engineer (Training Optimization) at Canva. Skills: scale and optimize the training system for our large-scale multimodal and foundation models, design distributed training systems using Megatron-LM, NVIDIA NeMo, FSDP, and Triton, pushing the limits of performance across compute, memory, and communication layers, sit at the intersection of systems and AI research, directly shaping how we train the models that will power Canva’s next generation of products, design, implement, and ”
What You'll Achieve.
scale and optimize the training system; improve all aspects of performance; unlock new levels of scalability; ship research that makes a real impact—from smart editing to AI video tools—at massive scale
Industry & Context.
Excellent problem-solving skills
What They're Looking For.
Must Have
systems-first engineer, deeply familiar with distributed model training at scale, understand the nuances of optimizing compute at every level of the stack, excited by challenges that stretch current boundaries, collaborator who communicates clearly across domains, background in LLMs, multimodal AI, or diffusion models, Proficiency in Python, Deep knowledge of PyTorch or JAX, Deep knowledge of libraries such as Megatron-LM, NeMo, or DeepSpeed, Familiarity with common optimization techniques such as FSDP/ZeRO, gradient checkpointing, or low-precision data types, Hands-on experience writing custom GPU kernels in CUDA or Triton, Excellent communication and problem-solving skills, full proficiency in English
Nice to Have
Familiarity with a system programming language (e. g. C++ or Rust)
What You'll Do.
and optimize large-scale machine learning systems for training
improve all aspects of performance
including GPU utilization
communication overhead
and memory efficiency
partner with research and modeling teams to align systems with algorithmic needs
evaluate and apply best practices for distributed training using industry-leading frameworks
dive deep into low-level optimization
including custom CUDA or Triton kernels
and fine-tune training workflows to unlock new levels of scalability
How You'll Work.
Team & Collaboration
partner with research and modeling teams; collaborate globally; communicates clearly across domains
Communication Scope
Excellent communication; full proficiency in English
Full Job Description
该岗位现面向所有经验阶段的候选人开放，包括社会招聘、应届毕业生，同时开放实习生岗位。工作地点为北京。欢迎申请，期待你的加入！ Notice: This position is open to candidates at all experience levels, including experienced candidates, 2025 and 2026 graduates, as well as internship opportunities. The role is based in Beijing. We welcome your application and look forward to having you on board! At Canva, we're building a future powered by AI that's as magical as it is impactful. As a Research Scientist at Canva, you'll be responsible for advancing the future of AI by experimenting with cutting-edge techniques, as well as improving models for real-world quality and performance. About the Group/Team We're the CORE team within the Generative AI supergroup. Our mission is to invent foundational technologies that will power the future of AI-assisted design. From large-scale models to groundbreaking research, our team builds the technical core of Canva’s creative intelligence engine. We collaborate globally to ship research that makes a real impact—from smart editing to AI video tools—at massive scale. Job Description About the Role/Specialty As a Machine Learning Engineer, you’ll lead efforts to scale and optimize the training system for our large-scale multimodal and foundation models. You’ll design distributed training systems using Megatron-LM, NVIDIA NeMo, FSDP, and Triton—pushing the limits of performance across compute, memory, and communication layers. You'll sit at the intersection of systems and AI research, directly shaping how we train the models that will power Canva’s next generation of products. What you’ll do (responsibilities) * You’ll design, implement, and optimize large-scale machine learning systems for training * You’ll improve all aspects of performance, including GPU utilization, communication overhead, and memory efficiency. * You’ll partner with research and modeling teams to align systems with algorithmic needs. * You’ll evaluate and apply best practices for distributed training using industry-leading frameworks.
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