Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 5 years of experience with full-stack development, including back-end, such as Java, Python, GO, or C++ codebases, and front-end, such as JavaScript, TypeScript, HTML, or CSS.
- Experience developing computer vision or machine learning products.
Preferred qualifications:
- Master's degree or PhD in Computer Science, or a related technical field.
- Experience with hacking hardware (e.g., Arduino, Raspberry Pi, etc.), and with other technologies (e.g., AS3, OpenCV, Android, Obj-C, etc).
- Experience deploying machine learning models on edge devices or in resource-constrained environments.
- Experience working in agricultural tech, bio-tech, or environmental science.
- Familiarity with spatial statistics or epidemiological modeling.
About the job
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
In this role, you will play a critical role in advancing our mosquito-born disease eradication program, directly contributing to vector control and public health initiatives. You will sit at the intersection of computer vision, statistical modeling, and software engineering. You will be solving complex biological problems and writing the production-ready code required to deploy those solutions into the real world.
Responsibilities
- Design, train, and deploy deep learning models to visually analyze mosquitoes in our production process.
- Build high-accuracy object detection and image segmentation pipelines to count mosquito pupae from complex, real-world image data.
- Leverage environmental data and biological parameters to model mosquito population dynamics, ultimately optimizing our mosquito release strategies and schedules.
- Take ownership of the full machine learning lifecycle and transition models from local research prototypes into scalable, highly available production environments.
- Work closely with entomologists, data scientists, and hardware engineers to leverage machine learning techniques to most effectively solve problems for the team.
