Sai Kumar Murali Krishnan

Experience

Canva Melbourne, VIC

Senior Applied Scientist March 2026 – Present

Built a video-agent evaluation platform spanning 104 tasks across seven suites, with multi-turn checks, LLM-based scoring, tracing, and a results viewer; enabled engineers and partner research teams to inspect changes and evaluate models independently.
Compared automated judgments with human reviews to identify gaps in creative-quality assessment; supported release evaluations and lower-cost model experiments, including separating narrative planning from execution.
Implemented and tested transcription serving changes using vLLM and integrated forced alignment; helped reduce processing time for an hour-long video from approximately five minutes to one minute while improving recognition quality and word timing.
Reduced LLM cost by 46% on a dialog evaluation by caching system prompts and tool definitions; deployed the caching integration to production.
Profiled document deserialization and removed repeated stack inspection, reducing validation time from 1,484 ms to 1.3 ms on the measured workload.

Applied Scientist / Machine Learning Engineer February 2024 – February 2026

Developed detail-preserving Background Generation methods to restore high-frequency image detail for a widely used Canva feature; named inventor on the resulting filed patent application.
Shipped a distilled-SDXL upgrade for Magic Expand before a release freeze, reducing end-to-end latency by approximately 50% through fewer denoising steps and GPU-resident image processing; evaluations showed fewer hallucinated people.
Adapted a SLURM/torchrun training stack to Ray/Anyscale, replacing local-data assumptions with an S3-streaming loader and validating distributed training with platform and research teams.

Machine Learning Engineer Intern November 2022 – February 2023

Developed and evaluated bias-mitigation methods for Stable Diffusion in Canva's text-to-image product, focusing on representation across gender and ethnicity.
Presented internship research as part of Generation Gap: Addressing Bias in Generative AI, SIGGRAPH Asia 2023.

Monash University Melbourne, VIC

Research Assistant November 2021 – July 2022

Built a genetic-programming and simulation framework in Julia to investigate a game-theoretic problem in scientific publishing.
Analyzed commit data from more than 30,000 deep-learning repositories using Python and the GitHub API to study the evolution of ML libraries for AutoML.

Monash University 2020 – 2023

Bachelor of Applied Data Science Advanced (Honours) Melbourne, VIC

First Class Honours (final honours grade: 90); Dean's List for academic excellence in 2021, 2022, and 2023.
Thesis: Bias Modelling and Mitigation in Diffusion Models.

Languages: Python, C/C++, Rust, Julia
Machine Learning: PyTorch, diffusion models, video agents, LLM evaluation, model training
Systems: Ray, Anyscale, SLURM, vLLM, AWS/S3, Weights & Biases, distributed training, efficient inference