Engineering Manager - Machine Learning (AI)

Location: Fully remote, based in Toronto, Canada

Salary: CAD 210,070–282,851 annual base salary, plus annual bonus and equity compensation

Company

hirify.global is a clinical-stage TechBio company developing medicines through an AI-native drug discovery and development platform that integrates biology, chemistry, and clinical development.

What you will do

• Lead a team building, scaling, and operating machine learning infrastructure for model development, training, deployment, and monitoring.
• Enable AI/ML, LLM, and agentic systems teams to work with massive datasets and complex deep learning workloads.
• Develop scalable infrastructure across on-premises and cloud supercomputing environments.
• Partner with ML engineering, data science, research, platform engineering, and business teams to translate requirements into robust infrastructure solutions.
• Mentor, coach, and sponsor engineers while contributing to technical leadership across MLOps, distributed computing, and infrastructure engineering.
• Improve GPU cluster utilization, agentic orchestration, model deployment reliability, and company-wide MLOps standards.

Requirements

• Hands-on technical experience as a tech lead or manager focused on infrastructure, MLOps, and distributed systems.
• Experience with machine learning infrastructure, model deployment, distributed compute, GPU optimization, and MLOps architecture.
• Ability to engage deeply with technical details involving machine learning, orchestration, and agentic systems.
• Experience with or willingness to learn Python, PyTorch, Docker, Kubernetes, Ray, Weights & Biases, Prefect, BigQuery, Postgres, GCP, CUDA, and model-serving frameworks.
• People-first leadership approach with a focus on mentoring, collaboration, learning, and accountability.

Nice to have

• Fluency in life sciences or drug discovery.

Culture & Benefits

• Fully remote work from Toronto, Canada.
• Annual bonus, equity compensation, and a comprehensive benefits package.
• Culture centered on integrity, direct collaboration, active learning, urgency, ownership, and accountability.
• Cross-functional collaboration across AI, scientific research, engineering, and business teams.