VPRND

Full Stack Developer - Hardware and Machine Learning

About Us:

BeniCann is an innovative company dedicated to revolutionizing the agricultural industry

through cutting-edge technology. Our prototype machine, designed to scan objects and identify

specific patterns, is a groundbreaking solution that aims to enhance crop health and

productivity. We are seeking a talented Full Stack Developer who can seamlessly blend

hardware development expertise with advanced machine learning skills to drive the evolution of

our product.

Responsibilities:

1. Hardware Development:

 Collaborate with the hardware engineering team to enhance and optimize the

existing plant-scanning machine.

 Troubleshoot and debug hardware-related issues.

 Propose and implement improvements to increase the efficiency and accuracy

of the scanning process.

2. Software Development:

 Take ownership of the existing Python codebase for the machine's operation and

refine it for better performance and maintainability.

 Develop and integrate machine learning algorithms to automate the detection of

mold on scanned plants.

 Implement data processing pipelines to handle large datasets efficiently.

3. Algorithm Development:

 Research, design, and implement state-of-the-art machine learning algorithms

for plant mold detection.

 Optimize algorithms for real-time processing on embedded systems.

 Continuously update and improve algorithms based on feedback and

performance analysis.

4. Cross-functional Collaboration:

 Collaborate with cross-functional teams, including hardware engineers, data

scientists, and product managers, to ensure seamless integration of software

and hardware components.

 Provide technical guidance to team members and actively participate in design

discussions.

Qualifications:

1. Bachelor's/Master's degree in Computer Science, Electrical Engineering, or related field.

2. Proven experience in both hardware and software development, with a focus on

embedded systems.

3. Strong proficiency in Python and experience with relevant libraries for machine learning

(e.g., TensorFlow, PyTorch).

4. Knowledge of image processing techniques and computer vision.

5. Familiarity with hardware communication protocols (e.g., GPIO, I2C, SPI).

6. Solid understanding of machine learning model deployment on embedded systems.

7. Excellent problem-solving and debugging skills.

8. Strong communication skills and ability to work collaboratively in a dynamic startup

environment.

Bonus Skills:

 Experience with edge computing and deployment.

 Familiarity with cloud services for data storage and processing.

 Previous work on agricultural technology or similar domains.

Application Notes: Interested candidates should submit their resume, a cover letter outlining

relevant experience, and any portfolio or code samples showcasing their hardware and

machine learning projects.