ABOUT US

Deep Genomics is at the forefront of using artificial intelligence to transform drug discovery. Our proprietary AI platform decodes the complexity of genome 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.

BASIC QUALIFICATIONS

• 3+ years of experience working as an ML Engineer, Software Engineer, or similar technical role focused on ML systems.
• Hands-on experience with modern ML frameworks, such as PyTorch or TensorFlow.
• Proficient in Python, with a strong grasp of software architecture, design patterns, and a deep understanding of engineering best practices.
• Experience with containerization and orchestration tools, such as Docker and Kubernetes.
• Ability to mentor and elevate other team members’ skills.

PREFERRED QUALIFICATIONS:

• Track record of shipping ML prototypes to production in fast-paced, iterative environments (e.g. startups or research-heavy teams).
• Familiarity with ML workflow orchestration and tracking tools, such as Weights & Biases, Metaflow, MLFlow, Kubeflow, Ray, or similar tools.
• Proficiency with cloud providers (preferably GCP), including managing compute, storage, and infrastructure for ML workloads.
• Experience working with biological or genomic data and applications.

• Build and scale ML workflows: Collaborate closely with ML scientists and data scientists to design, implement and maintain reliable systems for model training, evaluation, and inference.
• Enable experiment tracking and reproducibility: Integrate model development workflows with tools such as Weights & Biases.
• Engineer robust data pipelines: Develop and maintain data ingestion and processing pipelines for scalability, reproducibility, reliability.
• Prototype and iterate quickly: Partner with stakeholders to rapidly develop proof-of-concepts.
• Promote software engineering best practices: Drive high standards in code quality, modular design, testing and CI/CD.