Senior Software Engineer, Machine Learning, Debug

Google Google · Big Tech · Singapore

Senior Software Engineer focused on applying computer vision and deep learning models to analyze mosquitoes for a disease eradication program. The role involves designing, training, and deploying models for object detection and image segmentation, modeling population dynamics, and transitioning research prototypes to production environments. Requires full-stack development experience and experience with ML/CV products, with a preference for edge deployment and biological/environmental science contexts.

What you'd actually do

  1. Design, train, and deploy deep learning models to visually analyze mosquitoes in our production process.
  2. Build high-accuracy object detection and image segmentation pipelines to count mosquito pupae from complex, real-world image data.
  3. Leverage environmental data and biological parameters to model mosquito population dynamics, ultimately optimizing our mosquito release strategies and schedules.
  4. Take ownership of the full machine learning lifecycle and transition models from local research prototypes into scalable, highly available production environments.
  5. Work closely with entomologists, data scientists, and hardware engineers to leverage machine learning techniques to most effectively solve problems for the team.

Skills

Required

  • full-stack development
  • back-end development (Java, Python, GO, or C++)
  • front-end development (JavaScript, TypeScript, HTML, or CSS)
  • computer vision
  • machine learning products

Nice to have

  • Master's degree or PhD in Computer Science, or a related technical field
  • hacking hardware (e.g., Arduino, Raspberry Pi, etc.)
  • other technologies (e.g., AS3, OpenCV, Android, Obj-C, etc.)
  • deploying machine learning models on edge devices or in resource-constrained environments
  • agricultural tech, bio-tech, or environmental science
  • spatial statistics
  • epidemiological modeling

What the JD emphasized

  • production-ready code
  • production environments
  • deep learning models
  • computer vision
  • object detection
  • image segmentation

Other signals

  • production-ready code
  • scalable, highly available production environments
  • deep learning models
  • computer vision
  • object detection
  • image segmentation