Senior Software Engineer, Machine Learning, Debug

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

Source-linked last checked May 7, 2026

Salary

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401(k) match

Listed Source: EMPLR_CONTRIB_INCOME_AMT. source Last checked May 7, 2026.

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Schedule

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Weekend work

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Application

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Deadline

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About this role

Senior Software Engineer, Machine Learning, Debug Singapore 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. 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

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