What are the responsibilities and job description for the Senior ML Infrastructure Engineer position at Parametric (YC F25)?
About Us
Parametric is building robots to reliably automate physical labor in the real world. We’ve spent the last few months aggressively building our technology and fundraising and are now excited to begin rapidly growing the company.
About The Role
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.
This is a full-time in-person role in our San Francisco office.
What You’ll Do
We firmly believe the best version of the future includes everyone, so we encourage you to apply even if you don’t strictly meet all the requirements.
Parametric is building robots to reliably automate physical labor in the real world. We’ve spent the last few months aggressively building our technology and fundraising and are now excited to begin rapidly growing the company.
About The Role
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.
This is a full-time in-person role in our San Francisco office.
What You’ll Do
- 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
- 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
We firmly believe the best version of the future includes everyone, so we encourage you to apply even if you don’t strictly meet all the requirements.
Salary : $150,000 - $210,000