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What You'll Do
- Develop and maintain well-structured, reliable, and scalable codebases for end-to-end machine learning workflows.
- Take responsibility for the full lifecycle of training pipelines, including data preparation, reproducibility, and setting up robust training environments.
- Support the integration of ML models into production, ensuring they run efficiently and deliver consistent results.
- Partner with interdisciplinary teams (such as biology, optics, and software engineering) to align machine learning efforts with broader research and product goals.
Required Background & Experience
- Degree in Computer Science, Data Science, Engineering, or a related field (Bachelor’s or Master’s).
- 2–4 years of practical experience in developing and deploying deep learning or computer vision solutions.
- Strong Python skills, with hands-on expertise in PyTorch for model building.
- Experience building APIs (preferably with FastAPI).
- Solid grounding in software development practices: version control, modular code design, testing, and refactoring.
- Experience using Docker; understanding of Kubernetes for managing containers.
- Exposure to cloud platforms (AWS is a plus, though GCP or Azure are also relevant).
- Comfortable collaborating with multidisciplinary teams that include scientists and engineers.
- Working knowledge of SQL and relational databases.
- Experience creating scalable ML pipelines that are production-ready.
- Prior exposure to model optimization and deployment for efficiency and speed.
- Self-driven, proactive, and able to take ownership of projects in dynamic or uncertain environments.
Please click ‘apply’ or contact Jay Robins for any further information.
Email: [email protected]
Key Skills
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