ML Infrastructure Engineer – Edge AI at Scale

Laminar, Inc.; Boston, Northern (MA, KY); Hybrid; USD 160,000 - 190,000; Full time

Benefits offered by this job

Medical, Dental, Vision

401k with employer matching

Equity

Greentown Labs membership

Transportation benefit

Health and Wellness benefit

Flexible PTO

Job summary

Laminar, Inc. is seeking a ML Infrastructure Engineer to own the development of infrastructure and tooling for training, evaluating, and deploying models at scale. You will enable researchers to train on large data, validate on unseen data, and deploy champion models to factory edge devices.

You will build systems that scale Laminar’s ML models across domains, interfacing with researchers and engineers to deliver efficient, production-ready pipelines and tools for rapid experimentation.

Qualifications

• Experience building and scaling ML infrastructure across data pipelines.
• Proficiency with Python + ML tooling for experiment tracking and deployment.
• Track record of deploying models to edge devices and production.

Responsibilities

• Develop orchestration tooling for training, fine-tuning, and inference at scale.
• Create automated testing environments using semi-supervised metrics.
• Build model registries and deployment pipelines for edge devices.
• Develop monitoring to detect drift and trigger retraining pipelines.
• Collaborate with ML researchers and software engineers to integrate systems.

Skills

AWS

Databricks

MLflow

WandB

Python

boto3

databricks-sdk

Spark

SQL

Education

Bachelor's degree in CS/Engineering

Tools

Docker

Kubernetes

Parquet

Job description

Laminar, Inc. is seeking a ML Infrastructure Engineer to own the development of infrastructure and tooling for training, evaluating, and deploying models at scale. You will enable researchers to train on large data, validate on unseen data, and deploy champion models to factory edge devices.

You will build systems that scale Laminar’s ML models across domains, interfacing with researchers and engineers to deliver efficient, production-ready pipelines and tools for rapid experimentation.