Machine Learning Engineer, Gemini
Description For Machine Learning Engineer, Gemini
Google is seeking a Machine Learning Engineer for their Gemini team, focusing on conversational AI development. This role is crucial for developing next-generation technologies that impact billions of users' interactions. The position involves working on a conversational AI tool that enables users to collaborate with generative AI to enhance imagination, curiosity, and productivity. The ideal candidate will contribute to Google's innovative ML technologies, particularly in NLP and LLMs, while working in a fast-paced environment. The role offers opportunities to work on cutting-edge AI technology, collaborate with talented teams, and impact products at massive scale. Google provides a diverse and inclusive work environment, committed to equal opportunity and fostering a culture of belonging. The position requires strong technical expertise in machine learning, particularly in NLP/LLMs, combined with the ability to productionize advanced ML solutions and collaborate effectively with teams. This role represents an opportunity to shape the future of AI technology at one of the world's leading tech companies, working specifically on Gemini, a significant project in Google's AI portfolio.
Responsibilities For Machine Learning Engineer, Gemini
- Research and productionize state-of-the-art post-training and inference-time techniques
- Investigate the next generation of model improvements targeting short and mid-term time-horizons
- Collaborate and share results with the broader team
- Operate in a high-reward and fast-paced environment
Requirements For Machine Learning Engineer, Gemini
- Bachelor's degree in Computer Science or related technical field, or equivalent practical experience
- 2 years of experience with NLP/LLMs
- Experience with ML models, working with data, quality metrics, quality iterations
- Master's degree or PhD in ML (preferred)
- Experience with RL modeling (preferred)
- Experience with large-scale distributed model training, MLOps, ML infrastructure (preferred)
- Strong collaboration and communication skills (preferred)
- Strong track record of pushing state-of-the-art in ML (already in LLMs) (preferred)
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