Senior Software Engineer, Machine Learning, Debug

Google Google · Big Tech · Singapore

Senior Software Engineer, Machine Learning, Debug role focused on developing and deploying deep learning models for mosquito-born disease eradication. This involves computer vision for analyzing mosquitoes, statistical modeling for population dynamics, and building production-ready code for scalable deployment, with a focus on optimizing vector control strategies.

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
  • hacking hardware (Arduino, Raspberry Pi)
  • deploying machine learning models on edge devices
  • agricultural tech, bio-tech, or environmental science experience
  • spatial statistics
  • epidemiological modeling

What the JD emphasized

  • production-ready code
  • scalable, highly available production environments
  • deep learning models
  • object detection
  • image segmentation
  • environmental data
  • biological parameters
  • model population dynamics
  • optimize release strategies

Other signals

  • production-ready code
  • scalable, highly available production environments
  • deep learning models
  • object detection
  • image segmentation
  • environmental data
  • biological parameters
  • model population dynamics
  • optimize release strategies