Senior Machine Learning Engineer
| Company | Deep Genomics |
|---|---|
| Location | Cambridge, MA, USA, Toronto, ON, Canada |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | |
| Experience Level | Senior |
Requirements
- 3+ years of experience working as an ML Engineer, Software Engineer, or similar technical role focused on ML systems.
- Hands-on experience with ML frameworks, such as PyTorch, TensorFlow, or JAX.
- 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.
Responsibilities
- 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.
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.