Senior Research Scientist - Reinforcement Learning, MoEs

About

the role You’ll drive research directions and play a leading role in hands‑on work across the agent stack—from reward design and policy optimization to planning, memory, and tool orchestration, dataset construction, to post-training, and the development of novel post-training approaches. You’ll design tight experiments, iterate quickly, and land trustworthy conclusions. Most importantly, you’ll help convert research into reliable, safe, and high‑quality product experiences. What you’ll do Develop agent systems (planning, multimodal tool use, retrieval, novel training approaches, modeling ablations) for real tasks in design, vision, and language. Scale post-training and RL across distributed systems (PyTorch) with efficient data loaders, tracing/telemetry, stable training of mixture-of-experts (MoE) architectures, and reproducible pipelines; profile, debug, and optimize. Contribute to the research agenda for RL/agentic systems aligned with Canva’s product goals; identify high‑leverage bets and retire dead ends quickly. Build reward models and learning loops: RLHF/RLAIF, preference modeling, DPO/IPO‑style objectives, offline/online RL, curriculum learning, and credit assignment for multi‑step reasoning. Develop simulation and sandbox tasks that surface failure modes (planning errors, tool‑use brittleness, hallucination, unsafe actions) and turn them into measurable targets. Help align on rigorous evaluation for agents (task success, reliability, latency, safety, regressions). Stand up offline suites and online A/B tests; favor simple, controlled experiments that generalize. Collaborate and ship: work shoulder‑to‑shoulder with product, design, safety, and platform to land research as reliable features—then iterate. Share and elevate: mentor teammates, present findings internally, and contribute back to the community when it helps the field and our users. You’re likely a match if you have Depth in implementing and post-training MoEs/LLMs/VLMs/Diffusion models, with a track record of shipped research or publications in MoEs, RL or agents. Experience modifying, and adapting open-source models. Strong experience with experimental design: tight baselines, clean ablations, reproducibility, and clear, data‑backed conclusions. Fluency in Python and PyTorch; you’re comfortable in large ML codebases and can profile, debug, and optimize training and inference. Practical experience building agent loops (planning, tool invocation, retrieval, memory) and evaluating multi‑step reasoning quality. Hands‑on experience with policy optimization, reward modeling, and preference learning (e.g., RLHF/RLAIF, DPO/IPO, actor‑critic/PPO, offline RL). Experience with large‑scale training (distributed training, experiment tracking, evaluation harnesses) and cloud multimodal tooling. Experience with RL for MoE architectures.

Nice to have Experience with video and audio modelling. Experience with multi‑agent settings. Strength in alignment and safety evaluations, including red‑teaming and risk mitigation for tool‑using agents. Contributions to open‑source, benchmarks, or shared evaluation suites for agents. 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 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 info. Other stuff to know We make hiring decisions based on your experience, skills and passion, as well as how you can enhance Canva and our culture. When you apply, please tell us the pronouns you use and any reasonable adjustments you may need during the interview process. 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! Please note that interviews are conducted virtually. I'm interested I'm interested Privacy Notice I'm interested share this job Share to WeChat × Copy the link and open WeChat to share. Copy to clipboard Open WeChat Share to WeChat × Use Scan QR Code in WeChat and click ··· to share.

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