Aquabyte
Aquabyte
Posted 1 day ago
Internship
San Francisco, California
Hybrid
Smart Summary
The Research Intern will collaborate with the AI team to complete an end-to-end R&D project involving computer vision and machine learning systems for aquaculture. Responsibilities include designing experiments, training models, and translating research insights into production-ready approaches.
Aquabyte is looking for a Computer Vision Research Intern to collaborate with scientists and engineers on the AI team, working on end-to-end research projects related to computer vision and aquaculture. The ideal candidate should be enrolled in a Master's or PhD program in computer science, electrical engineering, or a related field, with a strong coding ability in Python and experience with neural networks and deep learning (ideally PyTorch). Initiative, autonomy, and good software engineering practices are also required.
Must Have Skills for ATS
Computer Vision
Machine Learning
Python
Deep Learning
PyTorch
3D Reconstruction
Stereo Vision
Software Engineering
Version Control
Data Processing
Model Optimization
Feature Extraction
Feature Matching
Disparity Estimation
Efficient Inference
Job Description
Our mission
Aquabyte is on a mission to revolutionize the sustainability and efficiency of aquaculture. It is an audacious, and incredibly rewarding mission. By making fish farming more efficient and viable, we aim to promote healthy (for the fish and environment) production of low carbon protein and mitigate one of the biggest causes of climate change. Aquaculture is the single fastest growing food-production sector in the world, and now is the time to define how technology is used to harvest the sea and preserve it for generations to come.
We are a diverse, mission-driven team that is eager to work alongside kindred spirits. If this vision inspires you please get in touch.
Our product
We are currently focused on helping salmon farmers better understand their fish population and make environmentally sound decisions. Through custom underwater cameras, computer vision, and machine learning we are able to quantify fish weights, detect the health status, and generate optimal feeding plans in real time. Our product operates at three levels: on-site hardware for image capture, cloud pipelines for data processing, and a user-facing web application. As a result, there are hundreds of moving pieces and no shortage of fascinating challenges across all levels of the stack.
Above all, Aquabyte is a customer-driven company. Our product development is dictated by the needs of fish farmers and we prioritize customer delight in everything we do. We are committed to building a global, collaborative team.
The role
Aquabyte’s AI team builds the computer vision and machine learning systems that observe millions of fish daily through our underwater camera fleet. We estimate fish weight via stereo 3D reconstruction, detect health conditions, and optimize feeding in real time. Our systems operate across on-site edge hardware, cloud pipelines and a customer-facing application.
As a Research Intern, you will collaborate closely with scientists and engineers on the AI team to work on an end-to-end research project at the intersection of computer vision and aquaculture. You will have the opportunity to work in a small, experienced team with domain expertise, and have access to data, resources and mentorship to complete an impactful R&D project that involves deciding methods, designing experiments, training models, and presenting your findings. Everything we do is connected to broader company goals so by the end of your internship we want this project to be ready to be deployed so that it can have impact across farms worldwide.
In office requirements: 2 days per week minimum
Duration: 10-12 weeks
Hours: Full-time
Work with your mentors to complete an end-to-end R&D Project over 10-12 weeks, from literature review through experimentation and a final deliverable.
Work on computer vision challenges in 3D reconstruction, efficient inference, visual recognition or model robustness - applied to real production data.
Design and run experiments, iterating with increasing depth.
Deliver a final presentation and a clean, elegant, reproducible codebase.
Collaborate with the wider team on translating research insights into production-ready approaches.
Enrolled in Masters or PhD program in computer science, electrical engineering, or a related field with a focus on Machine Learning or Computer Vision
Strong coding ability; strong grasp of Python
Have exposure to software engineering best practices (version control, testing, code reviews)
Experience with training neural networks / deep learning (ideally pytorch)
High degree of initiative; comfortable working autonomously on open-ended problems
Must return to degree program after the completion of the internship, or the internship fulfills a graduation requirement
At Aquabyte, we admire interesting people with a unique background. We strongly encourage you to apply even if you don’t satisfy all the requirements, and we will get back to you as soon as possible!
Aquabyte
Aquabyte is a leading provider of camera-based monitoring systems for fish farming, both at sea and on land. The system leverages AI and machine learning to deliver real-time data for fish farmers while reducing the amount of physical handling of the fish. We provide data on fish weight and growth, together with welfare indicators such as skin health and fin damage. Lice-counting through Aquabyte systems enables the farmers to better monitor lice, and take preemptive measures when needed, thus reducing the total impact of lice treatments. The newly launched Behaviour module in our systems provides insight on swim speed and swim tilt of the fish as well as breathing index. These elements can serve as early indicators for issues in the pen, as they inform the farmer of behaviour deviating from normal fish behaviour. Such early identification of potential challenges are especially important for closed or submerged production environments. With Aquabyte systems in the pen - fish farmers get valuable insights that enable better decision-making, improved fish health, increased efficiency, and more sustainable operations.
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