Machine Learning Engineer responsible for implementing and maintaining data science models in bpx’s machine learning studio. Bridging data science and computational needs to achieve business outcomes.
About the role
- AI/ML Infrastructure Architect at the Allen Institute developing engineering infrastructure for AI/ML applications. Collaborating with cross-functional teams to support bioscience research.
Responsibilities
- Develop and lead a cloud-agnostic state-of-the-art engineering infrastructure at the Allen Institute to support AI/ML research and applications.
- Procure and deploy GPUs to meet computational demands.
- Coordinate infrastructure implementation with external partners.
- Lead data management, software infrastructure and AI/ML workflow best practices and policies.
- Manage and lead a team of engineers.
- Develop and implement policies and software for efficient management, prioritization, and scheduling of AI workloads.
- Implement Cost Tracking and Reporting for transparency and prevent overruns.
- Collaborate with science unit teams to facilitate adoption and use of the new AI pipeline by providing training and support to accelerate the adoption process.
- Ensure integration of AI infrastructure with existing platforms.
- Develop and oversee a governance framework to ensure use of GPU resources align with the institutes scientific priorities.
- Regularly review and adjust resource allocation based on governance inputs.
- Help establish community standards for scalability in developing, disseminating, and evaluating AI/ML/computational methods for scientific problems.
- Participate in institute-wide initiatives, workshops, and seminars to promote engineering excellence through technical leadership, cross-disciplinary collaboration and knowledge sharing.
Requirements
- Bachelors Degree in Computer Engineering or related technical field or equivalent experience
- 7 years of experience working with MLOps in medium to large scale GPU clusters and/or cloud based ML deployments
- Experience with building, deploying and maintaining machine learning models
- Proficiency with cloud computing (AWS, GCP or Azure) and with on-prem clusters
- Experience with databases, large data management
- Working knowledge of AI/ML custom libraries, AI/ML execution platforms
- Proven ability to work independently and manage multiple projects simultaneously while meeting deadlines
- Excellent written and verbal communication skills, with the ability to collaborate effectively in a multidisciplinary team environment.
Benefits
- medical, dental, vision, and basic life insurance
- 401k plan
- Paid time off
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