Senior Machine Learning Operations Engineer (ML Ops)

Deep Genomics

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

Toronto, ON, Canada

Machine Learning

Senior Software Engineer

Hybrid

3+ years of experience

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Description For Senior Machine Learning Operations Engineer (ML Ops)

Deep Genomics is revolutionizing drug discovery through AI. As a Senior MLOps Engineer, you'll architect scalable systems to streamline ML pipelines from data ingestion to model deployment, accelerating the translation of insights into therapies. You'll work with world-class teams in Toronto and Cambridge, MA, shaping core infrastructure and prototyping innovative solutions.

Key responsibilities include:

Packaging and deploying models to production environments
Designing interfaces for complex deep learning models
Managing ML infrastructure on-premises and in the cloud
Rapid prototyping with ML scientists and stakeholders
Ensuring CI/CD best practices

Requirements:

3+ years experience in production ML systems and model deployment
Proficiency in Kubernetes, cloud technologies, and managing on-prem GPUs
Background in distributed systems, containerization, CI/CD
Self-motivated problem solver with excellent communication skills

Nice to have:

Startup experience bringing ML prototypes to production
Familiarity with bioinformatics data/pipelines
Knowledge of MLFlow, Kubeflow, Ray, Dask, Fluidstack, or similar tools

Deep Genomics offers:

Opportunity to transform drug discovery through AI
Competitive salary plus equity compensation (ESOPs)
Company-paid benefits
Flexibility in remote working
Exceptional learning and growth opportunities

Join us in building the future of AI-driven drug discovery and accelerate our ability to create life-changing therapies.

Last updated a year ago

Responsibilities For Senior Machine Learning Operations Engineer (ML Ops)

Package models, deploy to production environments, and monitor usage and performance
Design, implement, and deploy ways to make complex deep learning models usable by biologists and other domain specialists
Manage ML infrastructure, both on premises and in the cloud
Rapidly prototype POCs in partnership with ML scientists and other stakeholders
Test models at scale and ensure CI/CD best practices are followed by the whole team

Requirements For Senior Machine Learning Operations Engineer (ML Ops)

Kubernetes

3+ years experience in production ML systems, model deployment
Proficiency in Kubernetes, cloud technologies (AWS/Azure/GCP) and managing on-prem GPUs
Background in distributed systems, containerization, CI/CD
Self-motivated problem solver with excellent communication skills

Benefits For Senior Machine Learning Operations Engineer (ML Ops)

Competitive salary plus equity compensation (ESOPs)
Wide array of company-paid benefits
Flexibility in remote working
Exceptional opportunities for learning and growth