Deep Genomics

Deep Genomics is an AI-first TechBio organization pioneering a new path for genomic R&D with its AI foundation model platform. They aim to program therapies for any gene and any genetic condition by…

AI PlatformArtificial IntelligenceBigRNADrug DiscoveryGenetic MedicineGenomicsRNA TherapiesTechBiodeepgenomics.com

Deep Genomics

AI PlatformArtificial IntelligenceBigRNADrug DiscoveryGenetic MedicineGenomicsRNA TherapiesTechBiodeepgenomics.com

HQToronto, CA

Team Size105

Open Jobs6

Total Funding$237M

Latest Fundraise5 years ago

Join the Team

Senior MLOps Engineer

On-SiteToronto, Ontario, CA

On-Site • Toronto, Ontario, CA

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DevOps Engineer

ContractHamburg, DE

Contract • Hamburg, DE

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
You’ll 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- If this sounds like you, we would love to hear from you
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
You are someone who enjoys keeping the infrastructure running smoothly so that scientists can focus on their research
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
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
Extensive Hands-on experience with Kubernetes and containerization (Docker)
Familiarity with Python package and environment management (e.g., pip, conda, pixi)
Strong Python programming skills
Experience managing GPU compute (provisioning, debugging, driver management)
4+ years of experience operating production infrastructure
Proficiency with cloud platforms (GCP preferred; AWS/Azure acceptable) and Infrastructure-as-Code (Terraform)
Self-motivated problem solver with excellent communication skills
Solid background in CI/CD systems (CircleCI, GitHub Actions, or similar)
Understanding of ML frameworks (e.g., PyTorch, PyTorch Lightning), ML workflows (training, inference, evaluation), and the model lifecycle
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
Familiarity with MLOps tooling (e.g., W&B, Ray, VertexAI) and distributed compute patterns (e.g., DDP, realtime/batch inference, multi-node training).