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Parametric is building robots to reliably automate physical labor in the real world. As a Senior ML Infrastructure Engineer, you'll build the systems that power our entire autonomy stack. You'll design the infrastructure that enables our ML team to move fast, from data ingestion and model training to evaluation and deployment. Your work will directly determine how quickly we can iterate on models and ship improvements to robots in the field. This is an early-stage role where you'll define our ML infrastructure from the ground up. You'll work closely with research and systems engineers to build tooling that scales as we grow.
Job Responsibility:
Design and implement robust ML infrastructure for dataset management, model training and evaluation, and deployment
Collaborate with ML engineers to gather requirements and develop plans
Build and operate cloud infrastructure (e.g. AWS, GCP) for machine learning workloads for experiments and production
Automate model evaluation, selection, and deployment
Requirements:
Three or more years (or equivalent) working in devops, ML infrastructure, or platform engineering roles
Experience designing and implementing production-grade AI infrastructure
Deep understanding of the ML lifecycle: data pipelines, distributed training, model evaluation, and deployment
Strong proficiency with cloud platforms (AWS, GCP, or Azure) and infrastructure-as-code tools
Experience building CI/CD pipelines with tools like GitHub Actions, Jenkins, or similar
Comfortable with Python, bash, and infrastructure scripting