Minimum qualifications:- Bachelor's degree or equivalent practical experience.
- 2 years of experience with software development in Python and C programming languages, or 1 year of experience with an advanced degree.
- 2 years of experience applying mathematical modeling, numerical analysis, or statistical methods to solve engineering or scientific problems.
- 1 year of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
- 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 Computer Science or related technical fields.
- Experience with machine learning, statistical analysis, applied math, or operation research in the industry or in the academic sector.
- Experience productionizing machine learning systems or designing experiments.
- Experience with Google Ads systems or specialized ML tools such as TensorFlow, Keras, or TFX.
- Ability to to write high quality and low latency code/models that can train on and serve on every query.
About the jobAs a Software Engineer on the Proxybidder ML team, you will be involved in the full machine learning model lifecycle-from design and training to deployment and serving in production at the scale of billions of Search Ads. You will have the opportunity to innovate while collaborating with research teams to test and implement technologies using TPUs, Keras, TensorFlow, and JAX. Your work will directly impact the infrastructure and models that power nearly every Ads page on Google Search.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $147000 - $211000 (USD) 15% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities - Develop and maintain machine learning models using advanced AI techniques to predict user interactions and optimize advertiser Return on Investment (ROI).
- Innovate on machine learning model design to improve quality, stability, and efficiency throughout the entire model lifecycle.
- Analyze experiments using statistical methods to solve complex machine learning problems and improve model generalization.
- Enhance model health and stability by contributing to code health, automation, and alerting systems.
- Collaborate with Research and Infrastructure teams to test and implement the latest technologies in production environments.
