About the role
In your role as Staff Research Engineer (Generative Video) youll help bring Canvas next wave of AI-powered video creation to life turning cutting-edge generative video research into reliable scalable production-ready systems that delight hundreds of millions of users.
Youll sit at the intersection of applied research and engineering partnering closely with Research Scientists and product engineering teams to shape the end-to-end generative video stack from data and training to evaluation to inference and product integration. This is a hands-on Staff-level role where youll set technical direction make high-impact trade-offs and raise the bar on engineering excellence and operational maturity for generative video at Canva.
At the moment this role is focused on:
Working closely with Research Scientists to translate new generative video ideas into practical scalable implementations (e.g. diffusion-based video generation multimodal conditioning temporal consistency techniques)
Setting technical direction for generative video projects (text-to-video image-to-video video-to-video and video editing) aligning research bets with product needs safety expectations and platform constraints
Designing and building end-to-end training and inference pipelines evolving prototypes into robust systems with benchmarking monitoring regression testing and production guardrails
Driving quality and controllability improvements through rigorous experimentation including temporal coherence identity preservation prompt adherence and runtime performance
Engineering core model systems components for modern generative video approaches
Optimizing for scale and efficiency including distributed training performance mixed precision memory/throughput improvements batching and system-level latency/cost trade-offs in serving
Advancing evaluation benchmarking and data strategy improving reliability via dataset curation filtering deduplication captioning/annotation synthetic data and bootstrapped labeling
Strengthening operational excellence for production models: observability incident response root-cause analysis rollbacks prevention via automated checks and guardrails
Mentoring and uplifting others through design reviews code reviews experiment reviews and knowledge-sharing across engineering and research
Youre probably a match if you:
Thrive in ambiguity and enjoy owning complex end-to-end systems that bridge research and product engineering
Can make pragmatic trade-offs between quality controllability latency cost and safety and bring others along through clear technical communication
Care deeply about building systems that are not just impressive in demos but shippable scalable and dependable
Collaborate generously mentor others and raise engineering standards wherever you go
Were looking for someone who brings:
Strong experience building generative AI systems ideally in generative video or video editing (multimodal experience is a big plus)
Solid understanding of modern generative approaches (diffusion models Transformers/DiTs GANs) and how they behave in real-world pipelines
Strong working knowledge of multimodal learning including video-text/video-image conditioning VLM-style conditioning and/or retrieval-augmented conditioning
Staff-level engineering impact with a track record of leading technical initiatives across stakeholders driving alignment making trade-offs and delivering durable outcomes
Experience scaling training and inference including distributed training across large GPU fleets and a clear understanding of throughput/cost/infra trade-offs
Excellent engineering fundamentals: clean maintainable code testing discipline CI/CD workflows performance benchmarking and robust production observability
Scientific rigor and execution strength with the ability to design strong experiments validate hypotheses and improve model behavior using measurable evaluation frameworks
Strong proficiency in PyTorch and modern ML stacks and the ability to take research ideas/papers and implement them robustly
Bonus points (nice to have)
Experience with video editing models (inpainting/outpainting temporal masking object removal background replacement stylization relighting)
Experience with responsible gen-AI practices for video (safety filtering watermarking/provenance abuse mitigation robustness)
Experience with human automated evaluation loops (preference optimization reward models RLHF/DPO-style methods)
Deep inference optimization experience (quantization compilation streaming generation GPU memory optimization)
What youll learn and how youll grow
Deep involvement in Canvas long-term strategy for generative media and multimodal systems
The opportunity to set technical standards mentor others and shape our research-engineering culture
Direct product impact at global scale with pathways to ship meaningful improvements quickly
Space for exploration balanced with ownership and accountability for production outcomes
Additional Information :
Whats in it for you
Achieving our crazy big goals motivates us to work hard - and we do - but youll experience lots of moments of magic connectivity and fun woven throughout life at Canva too. We also offer a range of benefits to set you up for every success in and outside of work.
Heres a taste of whats on offer:
- Equity packages - we want our success to be yours too
- Health benefits plans to support you and your wellbeing
- 401(k) retirement plan with company contribution
- Inclusive parental leave policy that supports all parents & carers
- An annual Vibe & Thrive allowance to support your wellbeing social connection office setup & more
- Flexible leave options that empower you to be a force for good take time to recharge and supports you personally
Check out for more information.
Other stuff to know
We make hiring decisions based on your experience skills merit and business needs in compliance with applicable local laws. We celebrate all types of skills and backgrounds at Canva so even if you dont feel like your skills quite match whats listed above - we still want to hear from you!
When you apply please tell us the pronouns you use and any reasonable adjustments you may need during the interview process. Please note that interviews are conducted virtually.
At Canva we value fairness and we strive to provide competitive market-informed compensation whilst ensuring internal equity within the team in each region. The target base salary range for this position is $270000 - $310000. When calculating offers we make salary decisions based on market data your experience levels and internal benchmarks of your peers in the same domain and job level.
Remote Work :
No
Employment Type :
Full-time
About the roleIn your role as Staff Research Engineer (Generative Video) youll help bring Canvas next wave of AI-powered video creation to life turning cutting-edge generative video research into reliable scalable production-ready systems that delight hundreds of millions of users.Youll sit at the ...
About the role
In your role as Staff Research Engineer (Generative Video) youll help bring Canvas next wave of AI-powered video creation to life turning cutting-edge generative video research into reliable scalable production-ready systems that delight hundreds of millions of users.
Youll sit at the intersection of applied research and engineering partnering closely with Research Scientists and product engineering teams to shape the end-to-end generative video stack from data and training to evaluation to inference and product integration. This is a hands-on Staff-level role where youll set technical direction make high-impact trade-offs and raise the bar on engineering excellence and operational maturity for generative video at Canva.
At the moment this role is focused on:
Working closely with Research Scientists to translate new generative video ideas into practical scalable implementations (e.g. diffusion-based video generation multimodal conditioning temporal consistency techniques)
Setting technical direction for generative video projects (text-to-video image-to-video video-to-video and video editing) aligning research bets with product needs safety expectations and platform constraints
Designing and building end-to-end training and inference pipelines evolving prototypes into robust systems with benchmarking monitoring regression testing and production guardrails
Driving quality and controllability improvements through rigorous experimentation including temporal coherence identity preservation prompt adherence and runtime performance
Engineering core model systems components for modern generative video approaches
Optimizing for scale and efficiency including distributed training performance mixed precision memory/throughput improvements batching and system-level latency/cost trade-offs in serving
Advancing evaluation benchmarking and data strategy improving reliability via dataset curation filtering deduplication captioning/annotation synthetic data and bootstrapped labeling
Strengthening operational excellence for production models: observability incident response root-cause analysis rollbacks prevention via automated checks and guardrails
Mentoring and uplifting others through design reviews code reviews experiment reviews and knowledge-sharing across engineering and research
Youre probably a match if you:
Thrive in ambiguity and enjoy owning complex end-to-end systems that bridge research and product engineering
Can make pragmatic trade-offs between quality controllability latency cost and safety and bring others along through clear technical communication
Care deeply about building systems that are not just impressive in demos but shippable scalable and dependable
Collaborate generously mentor others and raise engineering standards wherever you go
Were looking for someone who brings:
Strong experience building generative AI systems ideally in generative video or video editing (multimodal experience is a big plus)
Solid understanding of modern generative approaches (diffusion models Transformers/DiTs GANs) and how they behave in real-world pipelines
Strong working knowledge of multimodal learning including video-text/video-image conditioning VLM-style conditioning and/or retrieval-augmented conditioning
Staff-level engineering impact with a track record of leading technical initiatives across stakeholders driving alignment making trade-offs and delivering durable outcomes
Experience scaling training and inference including distributed training across large GPU fleets and a clear understanding of throughput/cost/infra trade-offs
Excellent engineering fundamentals: clean maintainable code testing discipline CI/CD workflows performance benchmarking and robust production observability
Scientific rigor and execution strength with the ability to design strong experiments validate hypotheses and improve model behavior using measurable evaluation frameworks
Strong proficiency in PyTorch and modern ML stacks and the ability to take research ideas/papers and implement them robustly
Bonus points (nice to have)
Experience with video editing models (inpainting/outpainting temporal masking object removal background replacement stylization relighting)
Experience with responsible gen-AI practices for video (safety filtering watermarking/provenance abuse mitigation robustness)
Experience with human automated evaluation loops (preference optimization reward models RLHF/DPO-style methods)
Deep inference optimization experience (quantization compilation streaming generation GPU memory optimization)
What youll learn and how youll grow
Deep involvement in Canvas long-term strategy for generative media and multimodal systems
The opportunity to set technical standards mentor others and shape our research-engineering culture
Direct product impact at global scale with pathways to ship meaningful improvements quickly
Space for exploration balanced with ownership and accountability for production outcomes
Additional Information :
Whats in it for you
Achieving our crazy big goals motivates us to work hard - and we do - but youll experience lots of moments of magic connectivity and fun woven throughout life at Canva too. We also offer a range of benefits to set you up for every success in and outside of work.
Heres a taste of whats on offer:
- Equity packages - we want our success to be yours too
- Health benefits plans to support you and your wellbeing
- 401(k) retirement plan with company contribution
- Inclusive parental leave policy that supports all parents & carers
- An annual Vibe & Thrive allowance to support your wellbeing social connection office setup & more
- Flexible leave options that empower you to be a force for good take time to recharge and supports you personally
Check out for more information.
Other stuff to know
We make hiring decisions based on your experience skills merit and business needs in compliance with applicable local laws. We celebrate all types of skills and backgrounds at Canva so even if you dont feel like your skills quite match whats listed above - we still want to hear from you!
When you apply please tell us the pronouns you use and any reasonable adjustments you may need during the interview process. Please note that interviews are conducted virtually.
At Canva we value fairness and we strive to provide competitive market-informed compensation whilst ensuring internal equity within the team in each region. The target base salary range for this position is $270000 - $310000. When calculating offers we make salary decisions based on market data your experience levels and internal benchmarks of your peers in the same domain and job level.
Remote Work :
No
Employment Type :
Full-time
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