Sai Kumar Murali Krishnan

Experience

Canva Melbourne, VIC

Senior Applied Scientist March 2026 – Present

Built an agent evaluation platform adopted by research and engineering teams to compare models, detect regressions, and assess release quality, with automated scoring, execution traces, and a results viewer.
Evaluated candidate models for agent and generative-media tasks, comparing quality and inference cost through automated scoring and human review to inform model selection and releases.
Modernized transcription serving with vLLM and forced alignment, cutting processing time for an hour of video from approximately five minutes to one while improving recognition accuracy and word timing.
Reduced LLM inference cost by 46% on a conversation benchmark through prompt and tool-definition caching; shipped the optimization to production.
Built a headless Linux version of a motion-graphics engine and ported its build from CMake to Bazel, making it available for integration with an export service.
Served as a technical point of contact between research and product teams, reviewing model integrations, unblocking serving changes, and helping engineers run evaluations independently.

Applied Scientist / Machine Learning Engineer February 2024 – February 2026

Developed detail-preserving methods for Background Generation, a feature that reached over 1M monthly active users; named inventor on the resulting filed patent application.
Halved end-to-end outpainting latency for Magic Expand by deploying distilled SDXL and keeping image processing on the GPU, while improving visual quality in human evaluations.
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

Winter Research Assistant June 2022 – July 2022

Built a genetic-programming and simulation framework in Julia to investigate a game-theoretic problem in scientific publishing.

Summer Research Assistant November 2021 – January 2022

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, agent evaluation, model training, speech recognition
Systems: Ray, Anyscale, SLURM, vLLM, AWS/S3, Weights & Biases, distributed training, efficient inference