1 day ago
Responsibilities
- Own the deployment of ML models and engineering surrogates to customer production environments.
- Communicate results and trade-offs to senior stakeholders and influence product direction.
- Lead scoping and architecture design for data/ML systems, defining success metrics and delivery plans.
- Build robust and scalable ML systems, training and inference pipelines, and APIs.
- Mentor and develop engineers and data scientists, providing technical direction.
- Travel to customer sites for collaboration and solution building.
- Scope new projects and work-streams with existing customers.
Requirements
- At least 3 years of industry experience in a commercial, non-research environment.
- Proficiency in Python, PyTorch, Pandas, fastAPI, Scipy, and Kubeflow.
- Experience in deploying ML systems end-to-end and at scale.
- Ability to manipulate 3D point-cloud and mesh data for geometry-aware modeling.
- Strong problem-solving skills and ability to lead technical initiatives.
- Excellent communication skills for stakeholder engagement.