Computer vision data and science engineer at AdaViv | Torre

Computer vision data and science engineer

Emma highlights
This highlight was written by Emma’s AI. Ask Emma to edit it.
Full-time

Legal agreement: Employment

Base compensation
USD2k - 2.7k/month

+ Opportunity to grow.

+ Stock options.

+ 100% remote.

Non-negotiable
location_on
Remote (specific timezone)
public
GMT-06:00 to GMT-04:00
Posted over 4 years ago

Responsibilities


-What would you be doing? We seek highly motivated computer scientists and engineers with a solid technical background in machine learning/vision and a passion for agriculture or sustainability to join the growing team. As a Computer Vision & Data Science Engineer, you will be working with our Technical lead, Computer Vision Engineer, and Project Managers to build onto our Plant Computer Vision -to- Insights pipeline. You’ll have opportunities to work on fine-tuning CNN models, quantifying and tracking model results, and building predictive algorithms and data visualizations for crop performance. You will wear many hats and be exposed to many dimensions of building a deep-tech product. You will also gain unique experience working on a fast-moving startup project and learn the trade tricks with the potential to play a larger role as we grow. There will be an opportunity to grow a longer-term role. This role is open to graduate student internships, Co-ops, and Full-Time positions. -Responsibilities may include: The candidate must have hands-on experience in computer vision-specific deep learning using TensorFlow and python with significant contributions in research or industrial settings. -Data exploration and integration from different sensor data streams focused on multiple types of image data; explorative data visualization. -Build, parameterize, and hyper tune CNN models for plant disease detection -Improve on crop forecasting models. -Test and iterate model architectures and parameters to help accelerate model improvements. -Experimenting with Model Architecture tweaks to improve Model Performance on outside data. -Present your work internally and share key results with the team regularly. -Cloud automation and deployment of data pre-processing, modeling, and output You should be able to understand and produce long and complex texts and speeches with detailed descriptions in English.