Tech Lead, Search Discover, Core Modeling
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Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 8 years of experience with machine learning algorithms and tools (e.g., Jax/TensorFlow, deep learning, natural language processing, etc.).
- 8 years of experience in software development.
- 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 8 years of experience with data structures/algorithms.
- 3 years of experience in a technical leadership role leading project teams and setting technical direction.
- Experience with main components of recommendation systems (e.g., retrieval, ranking, re-ranking, cold-start, etc.).
- Experience with recommender systems, personalization, NLP, and retrieval.
- Excellent mathematical skills.
About the job
In Google Search, we're reimagining what it means to search for information – any way and anywhere. To do that, we need to solve complex engineering challenges and expand our infrastructure, while maintaining a universally accessible and useful experience that people around the world rely on. In joining the Search team, you'll have an opportunity to make an impact on billions of people globally.The US base salary range for this full-time position is $197,000-$291,000 + 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.Responsibilities
- Utilize Large Language Models (LLMs) reasoning capability to improve recommendation.
- Explore large embedding models and architecture, scaling law, scalable serving, knowledge distillation, and more.
- Develop the realtime training framework and conduct delayed feedback modeling.
- Train and serve ML architecture and efficiency optimization.
- Manage generative recommendation models.
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