Deep Genomics

Senior DevOps Engineer

Toronto, Ontario, Canada

Not SpecifiedCompensation
Senior (5 to 8 years)Experience Level
Full TimeJob Type
UnknownVisa
Biotechnology, Pharmaceuticals, Artificial Intelligence, Bioinformatics, Data ScienceIndustries

Senior DevOps Engineer

Employment Type: Full-time

Position Overview

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.

As a Senior DevOps 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 engineering, security, and compliance teams to implement and promote DevSecOps principles across the organization.

Basic Qualifications

  • 5+ years of experience working as a DevOps/MLOps engineer, SRE, or infrastructure engineer.
  • Proficient in Infrastructure as Code tools (e.g. Terraform and Helm) in public cloud environments.
  • 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 DevOps best practices.

Preferred Qualifications

  • Experience with Python/Shell scripting and automation tools.
  • 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.

Company Information

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.

Skills

Cloud Infrastructure
Terraform
Helm
Containerization
Orchestration
Monitoring
Logging
Security
DevSecOps
Data Pipelines
ML Platforms
Automation

Deep Genomics

AI-driven drug discovery and development

About Deep Genomics

Deep Genomics focuses on drug development in the biotechnology sector by utilizing artificial intelligence to explore RNA biology and discover potential therapies for genetic conditions. The company's main product, the AI Workbench, employs data-driven predictions to identify new drug targets. This tool has evolved over time, with the latest version, AI Workbench 3.0, set to enhance its capabilities in targeting complex genetic diseases. Deep Genomics serves a diverse clientele, including pharmaceutical companies and research institutions, and generates revenue through the development and licensing of its AI Workbench. The goal of Deep Genomics is to accelerate the drug discovery process and improve treatment options for patients suffering from genetic disorders.

Toronto, CanadaHeadquarters
2014Year Founded
$230.3MTotal Funding
SERIES_CCompany Stage
AI & Machine Learning, BiotechnologyIndustries
51-200Employees

Benefits

Company Equity
Health Insurance
Dental Insurance
Vision Insurance
Life Insurance
Disability Insurance
Professional Development Budget

Risks

Increased competition from companies like Insitro and Recursion Pharmaceuticals.
Rapid technological advancements may render current AI Workbench obsolete.
Ethical concerns and regulatory scrutiny could delay product development timelines.

Differentiation

Deep Genomics uses AI to unravel RNA biology for drug development.
The AI Workbench identifies novel drug targets and therapeutic candidates.
BigRNA model advances RNA disease mechanism discovery and candidate therapeutics.

Upsides

AI integration with CRISPR allows precise gene editing and therapeutic development.
AI-driven platforms optimize clinical trial designs, reducing costs and time to market.
AI identifies novel biomarkers, expanding target discovery capabilities.

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