AddressMinimum qualifications:
- PhD in Computer Science, a related technical field, or equivalent practical experience.
- 7 years of experience in Machine Learning (ML), ML Efficiency, or a related field.
- Experience with programming languages (e.g., Python or C/C++).
- Experience contributing to research communities including publishing in forums (e.g., ICML, ICLR, NeurIPS, or related).
- Experience in theoretical and empirical research and solving research problems.
- Have a passion for deep/Machine Learning, computational statistics, and applied mathematics
- Be self-directed and can drive new research ideas from problem abstraction, designing solution, experimentation, to productionisation in a rapidly shifting landscape
- Possess outstanding technical leadership and communication skills to conduct multi-team cross-team collaborations
At Google, research-focused Software Engineers are embedded throughout the company, allowing them to setup large-scale tests and deploy promising ideas quickly and broadly. Ideas may come from internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.
From creating experiments and prototyping implementations to designing new architectures, engineers work on real-world problems including artificial intelligence, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more. But you stay connected to your research roots as an active contributor to the wider research community by partnering with universities and publishing papers.
Google Research Australia is looking for Research Engineers who are interested in cutting-edge research in the domain of deep learning and large models.
At Google Research Australia, you will have opportunities to collaborate with Google teams all over the world and advance the state of the art in the above mentioned areas, enabling fundamental breakthroughs, and influencing next-generation AI-infused products reaching billions of users across the globe.
Google Research is building the next generation of intelligent systems for all Google products. To achieve this, we’re working on projects that utilize the latest computer science techniques developed by skilled software engineers and research scientists. Google Research teams collaborate closely with other teams across Google, maintaining the flexibility and versatility required to adapt new projects and foci that meet the demands of the world's fast-paced business needs.
Responsibilities- Work on radically new ML systems and model architectures that can improve capability of large foundational models and/or improve training, inference cost of such large models.
- Develop fundamentally novel optimization algorithms to improve training and generalization of large models.
- Work on fundamental advances to make inference with foundational models more efficient and flexible including knowledge adoption and distillation techniques
- Advance novel RL and similar techniques to make the models more safe, robust, and reliable.
- PhD in Computer Science, a related technical field, or equivalent practical experience.
- 7 years of experience in Machine Learning (ML), ML Efficiency, or a related field.
- Experience with programming languages (e.g., Python or C/C++).
- Experience contributing to research communities including publishing in forums (e.g., ICML, ICLR, NeurIPS, or related).
