About Us

Deep Genomics is at the forefront of using artificial intelligence to transform drug discovery. Our proprietary 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.

Opportunity

Join us in building the future of AI-driven drug discovery as a Senior MLOps Engineer. You will own and evolve the infrastructure that powers our ML pipelines – from cloud environments and CI/CD systems to workflow orchestration and model deployment. You will work closely with ML scientists, bioinformaticians, and software engineers to keep our platform reliable, reproducible, and scalable.

Ideal Candidate

You are someone who enjoys keeping the infrastructure running smoothly so that scientists can focus on their research. You are comfortable working across cloud platforms, CI/CD systems, containers, and GPUs – and you take pride in making these systems reliable and easy for others to use. You have 4+ years of experience in production infrastructure or MLOps, you write solid Python, and you are curious about the ML and scientific workflows your work supports. Above all, you are a collaborative, kind team member who communicates clearly, adapts to evolving needs, and is happy to help colleagues grow their own infrastructure skills along the way. If this sounds like you, we would love to hear from you.

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.

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.

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.

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.

Senior MLOps Engineer

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