Software Engineer AI/ML - Learning and Sustaina... @ Google
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Job Details
Overview
Google is seeking a Software Engineer AI/ML - Learning and Sustainability to develop next-generation technologies that impact billions of users. You will work on high impact projects, optimizing and deploying machine learning models across various domains including Search, YouTube, Gemini and DeepMind.
Minimum Qualifications
Bachelor’s degree or equivalent experience, plus relevant software development experience in one or more programming languages. Experience with speech/audio, reinforcement learning, ML infrastructure, or specialized ML fields is required.
Preferred Qualifications
Advanced degree in Computer Science or related field, with additional experience in data structures, algorithms, accessible technology, and other adjacent machine learning fields.
About the Role
As a Software Engineer AI/ML - Learning and Sustainability at Google, you will:
- Implement solutions in specialized ML areas and optimize models.
- Contribute to ML infrastructure including model deployment and evaluation.
- Work closely with product managers and team leaders to define quality goals.
- Innovate and generate new ideas in collaboration with Search/LearnX and DeepMind teams.
- Evaluate models using autoraters, human evaluations and live experiments.
Key Skills/Competency
Software Engineering, AI, ML, Reinforcement Learning, Data Processing, Model Optimization, Infrastructure, Evaluation, Scalable Systems, Innovation.
Compensation & Benefits
The US base salary range is $141,000-$202,000 with additional bonus, equity and benefits. Detailed salary information is discussed during the hiring process.
Equal Opportunity
Google is an equal opportunity workplace committed to diversification and inclusiveness in all aspects of employment.
How to Get Hired at Google
🎯 Tips for Getting Hired
- Customize your resume: Highlight relevant AI/ML and software engineering skills.
- Showcase project work: Detail contributions in ML model deployments.
- Research Google culture: Study their mission, values, and latest innovations.
- Prepare for technical interviews: Review algorithms, data structures, and ML frameworks.