Software Engineer, ML Infrastructure

Ideogram•Toronto, ON

22h•Onsite

About The Position

As a Software Engineer, ML Infrastructure at Ideogram, you'll build the systems that power training and serving for our generative AI models at scale. You'll work across the stack, from designing distributed training infrastructure to optimizing inference pipelines that serve millions of users, with a relentless focus on reliability, speed, and efficiency. We're looking for someone who combines deep systems expertise with a builder's mindset, strong technical ownership, and the ability to move fast in an evolving AI landscape.

Requirements

1-4 years developing and shipping large-scale production infrastructure.
Experience designing large, highly available distributed systems with Kubernetes/GCP, and GPU/TPU workloads on those clusters.
Experience in deploying, supporting, and troubleshooting in complex Linux-based computing environments.
Experience in worker scaling for training or inference workloads.
Fundamental knowledge in ML models and how they run on GPUs.
Deep sense of ownership - proactively identifies opportunities, suggests improvements, and acts on them.
Grit and adaptability to solve complex technical challenges that evolve day to day.
Ability to explain technical concepts to both engineers and non-technical stakeholders.
Pushes for quality through constructive code review and collaboration.
Bachelor's degree in Computer Science, Engineering, related field, or equivalent practical experience.

Responsibilities

Build the systems that power training and serving for generative AI models at scale.
Design distributed training infrastructure.
Optimize inference pipelines that serve millions of users.
Focus on reliability, speed, and efficiency.
Combine deep systems expertise with a builder's mindset.
Demonstrate strong technical ownership.
Move fast in an evolving AI landscape.

Benefits

Competitive compensation and equity
4 weeks of vacation
Comprehensive health, vision, and dental coverage
RRSP/401(k) with employer match up to 4%
Top-of-the-line tools and tech
Daily in-office lunches and dinners
Autonomy to explore and experiment
Access to compute/resources for new ideas, large-scale experiments, or research with a clear business or creative use case.
Culture of learning and growth
Mentorship