Canva

AI

MachineLearningEngineer(TrainingOptimization)

Beijing, Beijing, China FULL TIME
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for mid candidates.

The Brief

“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, pushing the limits of performance across compute, memory, and communication layers, systems and AI research, train the models that will power Canva’s next generation of products. design, implement, and optimize large-scale machine learning systems for training. improve all aspects of performance, incl”

What You'll Achieve.

improve all aspects of performance; unlock new levels of scalability

Industry & Context.

AI
Problems you'll solve

problem-solving skills

What They're Looking For.

Must Have

background in LLMs, multimodal AI, or diffusion models, Proficiency in Python, Deep knowledge of PyTorch or JAX, Hands-on experience writing custom GPU kernels in CUDA or Triton, Excellent communication and problem-solving skills, incl. full proficiency in English

Nice to Have

Familiarity with a system programming language (e. g. C++ or Rust) is a plus, Familiarity with common optimization techniques such as FSDP/ZeRO, gradient checkpointing, or low-precision data types

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

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 to align systems with algorithmic needs; collaborate globally to ship research that makes a real impact; communicates clearly across domains

Communication Scope

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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