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Software Engineer III, AI/ML, Shopping Targeting
Google • Mountain View, CA, USA
Skills & Technologies
Job Description
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 2 years of experience with software development in C++, or 1 year of experience with an advanced degree.
- 1 year of experience with machine learning and deep learning.
- 1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
Preferred qualifications:
- Master's degree or PhD in deep learning, computer vision, NLP, speech, or a related technical field.
- 2 years of experience with data structures and algorithms.
- Experience or research in ads, search, or recommendation systems.
- Experience with AdsML, PyTorch, TFX, Tensorflow, Jax, or Keras.
- Experience developing accessible technologies.
The US base salary range for this full-time position is $141,000-$202,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.
- Write product or system development code.
- Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
- Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
- Develop and refine Machine Learning techniques to improve Shopping Ads targeting performance and quality, and investigate new technologies to enhance personalization for Shopping Ads.
- Manage the full ML model life-cycle: training, evaluation, deployment, and identification of improvement opportunities (e.g., new architectures, data, signals, evaluation methods).
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