Senior Infrastructure Engineer

deepgenomics; Toronto; On-site; CAD 100,000 - 125,000; Full time

Job summary

A cutting-edge biotechnology company in Toronto seeks a Senior Infrastructure Engineer to build and optimize infrastructure for machine learning models and applications. The role involves managing cloud and on-premises systems, implementing DevSecOps practices, and collaborating across teams. With a competitive compensation package and a focus on growth, this position is ideal for experienced professionals in infrastructure management.

Benefits

Highly competitive compensation

Comprehensive benefits

Flexible work environment

Learning and development budget

Qualifications

• 5+ years of experience in Infrastructure Engineering or related fields.
• Proficiency in Infrastructure as Code tools.
• Deep expertise in containerization and orchestration.

Responsibilities

• Manage infrastructure for software, data, and ML platforms.
• Design integrations to ensure seamless data flow.
• Implement DevSecOps principles.

Skills

Infrastructure management

DevOps/MLOps

Containerization

CI/CD pipeline management

Mentoring

Scripting

Tools

Terraform

Kubernetes

Docker

Google Cloud Platform

CircleCI

Job description

About Us

Deep Genomics is at the forefront of using artificial intelligence to transform drug discovery. Our cutting-edge AI platform decodes the complexity of RNA biology to identify novel drug targets, mechanisms, and therapeutics inaccessible through traditional methods. With expertise spanning machine learning, bioinformatics, data science, engineering, and drug development, our multidisciplinary team in Toronto and Cambridge, MA is revolutionizing how new medicines are created.

About the Role

As a Senior Infrastructure Engineer, you will play a key role in building, scaling, and optimizing the infrastructure and tooling that empowers our diverse scientific and engineering teams. You will enable seamless development of our sophisticated ML models, software applications, and data pipelines. Through close collaboration with teams across engineering, machine learning, and biology, you\'ll help push the boundaries of drug discovery through thoughtfully engineered systems.

Key Responsibilities

• Manage infrastructure for software, data, and ML platforms, both in the cloud and our on-premises GPU clusters.
• Design and implement integrations between infrastructure components (containing internal and 3rd party systems) to ensure seamless flow of data in a robust, reliable, and secure manner.
• Streamline and / or automate operational tasks such as infrastructure provisioning, configuration management, and application deployment.
• Implement and manage robust monitoring, logging, and alerting for key infrastructure components.
• Collaborate closely with engineers, scientists, security, and compliance teams to implement and promote DevSecOps principles across the organization.

Basic Qualifications

• 5+ years of experience working as an Infrastructure Engineer, DevOps / MLOps Engineer, or SRE.
• Proficient in architecting and managing infrastructure using Infrastructure as Code tools (e.g. Terraform and Helm).
• Deep expertise in containerization and orchestration technologies like Docker and Kubernetes.
• Strong understanding of identity management and security best practices.
• Extensive experience designing, implementing, and maintaining CI / CD pipelines (e.g. CircleCI).
• Demonstrated experience with mentoring and elevating other team members\' skills to adhere to engineering and DevOps best practices.

Preferred Qualifications

• Experience with Python / Shell scripting and automation tools.
• Experience managing infrastructure on Google Cloud Platform (GCP).
• Hands-on experience with modern ML platforms and frameworks (e.g. Weights & Biases, Metaflow, MLflow, Ray) and familiarity with the operational challenges of scaling ML workloads.
• Experience designing and operating hybrid-cloud architectures that span on-premises and cloud environments, with an emphasis on resilience, observability, and cost optimization.
• Familiarity with secrets management, zero-trust architectures, and secure-by-default design patterns in regulated or privacy-sensitive environments.

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.

Deep Genomics welcomes and encourages applications from people with disabilities. Accommodations are available on request for candidates taking part in all aspects of the selection process.

Deep Genomics thanks all applicants, however only those selected for an interview will be contacted.