Senior MLOps Engineer

Team: Engineering

Location: Toronto, Ontario

Commitment: Full-time

Workplace Type: hybrid

Key Responsibilities

Maintain and improve cloud infrastructure (GCP) using Infrastructure-as-Code tools (Terraform).
Manage IAM, RBAC, and permission policies across cloud environments.
Own and evolve CI/CD pipelines (CircleCI, GitHub Actions) and ensure best practices are followed across the engineering and ML teams.
Administer and support workflow orchestration platforms (e.g., Seqera/Nextflow, Argo, Kubeflow).
Operate and configure ML experiment tracking and registry tooling (e.g., W&B, MLflow).
Build and maintain containerized environments (Docker) and manage Kubernetes clusters.
Manage GPU resources – provisioning, scheduling, and debugging hardware and driver issues.
Write and maintain Python tooling, scripts, and integrations that support ML infrastructure.
Help deploy ML models to production environments and monitor their performance.

Basic Qualifications

4+ years of experience operating production infrastructure.
Proficiency with cloud platforms (GCP preferred; AWS/Azure acceptable) and Infrastructure-as-Code (Terraform).
Extensive Hands-on experience with Kubernetes and containerization (Docker).
Solid background in CI/CD systems (CircleCI, GitHub Actions, or similar).
Experience managing GPU compute (provisioning, debugging, driver management).
Familiarity with Python package and environment management (e.g., pip, conda, pixi).
Strong Python programming skills.
Self-motivated problem solver with excellent communication skills.

Preferred Qualifications

Understanding of ML frameworks (e.g., PyTorch, PyTorch Lightning), ML workflows (training, inference, evaluation), and the model lifecycle.
Familiarity with MLOps tooling (e.g., W&B, Ray, VertexAI) and distributed compute patterns 
(e.g., DDP, realtime/batch inference, multi-node training).
Familiarity with Kubernetes CRDs and batch/gang schedulers (e.g., Volcano, Kueue).
Experience working with large-scale datasets (storage, versioning, efficient access patterns).
Experience working directly with scientists and researchers in an interdisciplinary setting.
Knowledge of biology and/or machine learning science.
Familiarity with data compliance and governance frameworks (e.g., HIPAA, SOC 2).
Previous startup experience.

What We Offer

A collaborative and innovative environment at the frontier of computational biology, machine learning, and drug discovery. 
Highly competitive compensation, including meaningful stock ownership.
Comprehensive benefits - including health, vision, and dental coverage for employees and families, employee and family assistance program. 
Flexible work environment - including flexible hours, extended long weekends, holiday shutdown, unlimited personal days.
Maternity and parental leave top-up coverage, as well as new parent paid time off. 
Focus on learning and growth for all employees - learning and development budget & lunch and learns.
Facilities located in the heart of Toronto - the epicenter of machine learning and AI research and development, and in Kendall Square, Cambridge, Mass. - a global center of biotechnology and life sciences.