🚀 The company

Arvist is providing critical visibility to minimize supply chain disruptions and reduce costs without any new infrastructure costs. Using existing security cameras and combining it with the power of Digital Twin and AI, our proprietary technology can unlock unprecedented visibility into the warehousing and logistics operations, gaining real-time insights on productivity, safety, and response.

🎉 The role

We're seeking an Backend/ML Developer to join our engineering team for opportunity to learn more about modern AI spaces and how models are executed in enterprise environments!

The Backend/ML Engineer role brings Arvist’s customer value together by combining streams from RTSP protocols with ML to create reportable data using real-time feed via ML models and mathematical analysis (geometry, algebra, and classic programming). ML models are only one tool used to detect anomalies, and must be used in conjunction with mathematics and potentially consecutive ML models to define thresholds, distances, and events (observations, reports, etc.) within Arvist’s infrastructure. These processes help partners customize and define processes within a worker facility to create a digital twin system. Customers define which regions of an IP camera are processed by specific ML models and workflows to reduce redundancy and mismarked observations. Customers can define interval and schedule based tasks in a generalizable way (via celery beat) to control how often a workflow is run on specific cameras (fps) and if it is only run during certain operating hours (for fine-grained control of operations). A primary goal for Arvist is for any workflow we create for customers to be generalizable to any camera with independent scheduling and regions within its related camera(s).

Women and under-represented minorities strongly encouraged to apply.

Note: We are filling 2 roles for this job description, one is more entry level or internship level and other is a more senior/experienced engineer role.

🥷🏼 Skills

Backend:

Python HTTP API’s (FastAPI)
Python Task Management (Celery + Celery Beat)
Asynchronous Programming
Postgres (asyncpg)
Processing RTSP frames (open-cv)

ML (Image Segmentation, Image Classification, and Object Detection):

Hugging Face Transformers Pipeline (for hybrid on-prem/cloud models)
AWS Sagemaker Inference or Google AI Platform (for cloud-only models)
Developing a range of computer vision based models with fast turnaround time
Processing and refining datasets for creating generalizable and accurate ml models
Experience utilizing geometry, classical programming, and vision-based mathematics to produce reports and observations for customers that hold value or non-obvious insight. This means utilizing a deployed model within our backend service (FastAPI, Celery, and Celery Beat) to bridge model inference with insightful behavior.