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Google

Research Scientist, ML Efficiency, Google Research

Google

Published 14 May 2026
Singapore
Full Time
27500 - 275000 people

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Role Highlights

Languages used

Key skills

Deep Learning
NLP
Data Mining
Reinforcement Learning
Machine Learning
Diffusion Models
Computer Science
Technical Leadership
Applied Mathematics
LLMs
Generative AI
Research
PhD
Transformers
Architecture
Statistics
Infrastructure
ProtoTyping
Compilers
Search
Inference
Optimization
Deployment
pre-training

Tools, Libraries and Frameworks

Description

The role involves setting up large-scale tests and deploying research ideas to develop new products, processes, or technologies. The individual will design new architectures, create experiments, and prototype implementations to address real-world problems. Responsibilities include advancing algorithms, sampling techniques, and large-scale optimization to improve the efficiency of generative AI models. The scientist will collaborate with hardware and software teams to optimize kernels, inference engines, and model deployment pipelines. Additionally, the role requires contributing to the wider research community by sharing and publishing findings.

Required Qualifications and Skills

Candidates must possess a PhD in Computer Science or a related field, or have equivalent practical experience. Applicants are required to have at least one scientific publication submission for conferences, journals, or public repositories. The role necessitates experience in driving progress, solving problems, and mentoring junior team members. Proficiency in English is a requirement for the position.

Disclaimer

Disclaimer: Job and company description information and some of the data fields may have been generated via GPT-4 summarisation and could contain inaccuracies. The full external job listing link should always be relied on for authoritative information.

About the company

Google

Size

275773

Website

goo.gle

HQ

Mountain View, US

Public/Private

Public Company

Description

At gTech's Users and Products team, innovations are focused on enhancing user engagement and solving complex customer needs through technical expertise and a deep understanding of Google's and Alphabet's broad product environments. The team acts as a bridge between Google’s users and its product teams, ensuring that user insights are integrated into product offerings which support numerous annual product launches. The newly formed Machine Learning Data Operations (MLDO) team within gUP Operations plays a critical role in delivering and tuning machine learning and GenAI data operations across Google’s product suite, leveraging extensive global vendor networks. Google's overarching goal is to create impactful products and services, with gTech playing a strategic role in bringing these solutions to fruition through technological and operational expertise.

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