Staff Research Engineer - Generative Video

Canva • San Francisco, CA

1d • Hybrid

About The Position

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

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
• Collaborate generously, mentor others, and raise engineering standards wherever you go
• 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

Nice To Haves

• 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)

Responsibilities

• 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

Benefits

• 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

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed
• Number of Employees: 5,001-10,000 employees