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We have been a pioneer in technology and innovation for over 80 years, and we are committed to using our expertise to help businesses and organizations transform for the digital age.

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We are committed to contributing to the United Nations Sustainable Development Goals (SDGs).

These goals are a blueprint for a better future for all, and we believe that technology can play a vital role in achieving them.

If you share our passion for making a meaningful impact on the world, we invite you to join our global family of 130,000 employees spanning more than 50 countries.

We are a diverse workforce, and we offer a wide range of opportunities for you to grow and develop your career.

Together, we can create a more sustainable future for all.

Researcher in Self-Improving Machine Learning

We are seeking creative and intellectually ambitious researchers to join our multidisciplinary Self-Improving ML team in Silicon Valley.

Our goal is to develop AI systems that can improve how AI itself is built.

We study fundamental questions at the intersection of learning, generalization, data, and automated discovery:

- What determines whether a model generalizes beyond its training data?

- How can an AI system identify or generate the data it needs to improve?

- Can we automate the creation of new models, learning algorithms, and research ideas?

Our team brings together researchers from different disciplines like math, neuroscience, physics and computer science.

Rather than expecting everyone to fit the same profile, we look for people who can contribute a strong perspective while collaborating across disciplines.

This role is particularly suited to researchers who enjoy pursuing deep, open-ended questions, building and testing unconventional ideas, and translating fundamental research into working AI systems.

Responsibilities :

Researchers have substantial freedom to develop new directions based on their interests and the evolving needs of the program.

These are some examples of possible research directions :

- Develop and prototype algorithms that enable AI systems to improve their models, learning strategies, data, or problem-solving processes.

- Investigate the relationship between training data and generalization, including methods for automatically selecting, generating, or refining data.

- Explore approaches for automating elements of creativity, scientific discovery, and ML research.

- Design rigorous experiments to evaluate novel hypotheses and understand why methods succeed or fail.

Essential Qualifications :

- PhD, or near completion of a PhD, in machine learning, computer science, neuroscience, mathematics, statistics, physics, or a related field.

- A demonstrated ability to develop original ideas or substantially improve existing methods, as shown through research publications, projects, or comparable accomplishments.

- Strong interest in fundamental questions involving learning, generalization, data, automated discovery, or self-improving systems.

- Ability to independently formulate and pursue a research direction and carry a long-term project from hypothesis to experimental validation.

- Experience with at least one deep-learning framework, such as PyTorch or TensorFlow.

- Strong collaboration skills and enthusiasm for working with researchers from different technical backgrounds.

- Ability to communicate research ideas clearly and translate them into well-designed experiments and reliable implementations.

Preferred Qualifications :

- Experience conducting interdisciplinary research.

- Expertise that complements conventional ML research, such as neuroscience, mathematics, statistics, physics, optimization, or automated scientific discovery.

- Experience developing high-performance implementations of deep-learning algorithms.

- Experience with large-scale experimentation or distributed training and inference.