Staff Research Engineer - Generative Video
Canva
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San Francisco, CA
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Mid-Senior level
Posted March 28, 2026
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Responsibilities
Commitments
Responsibilities
- You’ll 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 you’ll 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
- Thrive in ambiguity and enjoy owning complex, end-to-end systems that bridge research and product engineering
- Collaborate generously, mentor others, and raise engineering standards wherever you go
Commitments
Our head office is in Sydney, Australia, but San Francisco is now home to our US operations.
The role is listed as hybrid, meaning we are flexible and empower you to work where you prefer - whether that's at home or at the office.
We’re Looking For Someone Who Brings
Please note that interviews are conducted virtually.
Not Met Priorities
What still needs stronger evidence
Requirements
- 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
- 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
Preferred Skills
- 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)
- Deep involvement in Canva’s 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
Company Description
Join the team redefining how the world experiences design.
Hey, g'day, mabuhay, kia ora,你好, hallo, vítejte!
Thanks for stopping by. We know job hunting can be a little time consuming and you're probably keen to find out what's on offer, so we'll get straight to the point.
Where And How You Can Work
Our head office is in Sydney, Australia, but San Francisco is now home to our US operations. The role is listed as hybrid, meaning we are flexible and empower you to work where you prefer - whether that's at home or at the office.
Job Description
About the role
In your role as Staff Research Engineer (Generative Video), you’ll help bring Canva’s 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.
You’ll 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 you’ll 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
You’re 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
We’re 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 You’ll Learn And How You’ll Grow
Deep involvement in Canva’s 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
What's in it for you?
Achieving our crazy big goals motivates us to work hard - and we do - but you'll 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.
Here's a Taste Of What's 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 lifeatcanva.com 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 don’t feel like your skills quite match what’s 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 $270,000 - $310,000. 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.
Join the team redefining how the world experiences design.
Hey, g'day, mabuhay, kia ora,你好, hallo, vítejte!
Thanks for stopping by. We know job hunting can be a little time consuming and you're probably keen to find out what's on offer, so we'll get straight to the point.
Where And How You Can Work
Our head office is in Sydney, Australia, but San Francisco is now home to our US operations. The role is listed as hybrid, meaning we are flexible and empower you to work where you prefer - whether that's at home or at the office.
Job Description
About the role
In your role as Staff Research Engineer (Generative Video), you’ll help bring Canva’s 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.
You’ll 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 you’ll 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
You’re 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
We’re 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 You’ll Learn And How You’ll Grow
Deep involvement in Canva’s 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
What's in it for you?
Achieving our crazy big goals motivates us to work hard - and we do - but you'll 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.
Here's a Taste Of What's 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 lifeatcanva.com 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 don’t feel like your skills quite match what’s 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 $270,000 - $310,000. 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.