Mid-Level ML Engineer (Computer Vision Systems)

We are seeking a seasoned Machine Learning Engineer – Computer Vision to design, optimize, and deploy deep learning models for large scale, real time edge inference. In this role, you will work on the end to end lifecycle of computer vision models—from training and evaluation to optimization, automated governance, and edge deployment—while advancing MLOps capabilities on Google Cloud. You will work at the intersection of deep learning, cloud infrastructure, and edge AI, building reliable, high performance solutions that scale across devices and continuously improve through automation and data driven evaluation.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.

-2+ years in Machine Learning Engineering, with an emphasis on Computer Vision.
-Deep, hands-on expertise with TensorFlow 2.x and/or PyTorch.
-Proven experience optimizing deep learning models for edge devices using TFLite (e.g., post-training quantization, pruning, handling custom ops).
-Strong proficiency in Google Cloud Platform, specifically building and running custom components in Vertex AI Pipelines (KFP).
-Advanced programming skills in Python, with experience containerizing ML workloads using Docker.

-Hands-on experience with the Ultralytics YOLOv8 ecosystem, specifically bridging PyTorch YOLO weights to TensorFlow/TFLite edge deployments.
-Experience using Google Cloud Composer (Apache Airflow) to schedule and trigger complex ML training pipelines based on data arrival or model drift.
-Familiarity with Google Cloud Dataflow (Apache Beam) for large-scale, parallelized image preprocessing, augmentation, and dataset formatting (e.g., generating TFRecords).
-Knowledge or experience in Generative AI architectures, with experience building Retrieval-Augmented Generation (RAG) pipelines and developing multi-agent systems.

Benefit packages for this role will start on the 1st day of employment and include medical, dental, and vision insurance, as well as HSA, FSA, and DCFSA account options, and 401k retirement account access with employer matching. Employees in this role are also entitled to paid sick leave and/or other paid time off as provided by applicable law.