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Staff Applied Scientist - Search - job 1 of 2

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About Robin AI

Robin AI is on a mission to rebuild the legal industry — starting with making contracts simple for everyone. We are a pioneer in Legal AI, built on proprietary models, licensed data, and deep partnerships with Anthropic and AWS. Since 2019, we’ve expanded our footprint to 4 continents and have been supporting many of the world’s most successful businesses, including GE, Pfizer, KPMG, and UBS.

What will you do as a Staff Research Scientist - Search?

You will be leading, designing and experimenting with cutting edge research on how to best solve pressing issues surfacing in the legal domain. You will apply your expertise in machine learning, data science, and behavioural modelling to drive meaningful insights and innovations.

As AI agents become the norm in this industry, so does the need for curating and creating personalised experiences, and with that understanding the reasoning behind the models’ decisions. We are looking for people with a background and interest in advancing understanding in large language models, controllability, recommender systems or other aspects of explainability and personalisation.

Your day-to-day responsibilities:

Develop methods for understanding LLMs by reverse engineering algorithms learned in their weights.
Develop and evaluate algorithms that enhance the transparency of black-box models, enabling better understanding of decision-making processes.
Performing fine-tuning and reinforcement learning to teach language models how to interact with new information architectures.
Building “hard” eval sets to help identify failure modes of how language models work with legal data.
Build infrastructure for running experiments and visualising results.
Work with colleagues to communicate results internally and publicly.
Stay updated with the latest research in machine learning, AI, and interpretability or personalisation to bring innovative solutions to the table.
Mentor junior researchers and contribute to building a collaborative, knowledge-sharing culture.

Ideally, you should have the following qualifications:

A Ph.D. in Computer Science, Data Science, Machine Learning, Statistics, or a related field (or equivalent practical experience).
Direct working experience on interpretability, personalisation techniques, recommender systems, or large-scale data analytics.
Strong expertise in machine learning algorithms, statistical methods, and optimisation techniques.
Have a strong track record of scientific research (in any field) background
You view research and engineering as two sides of the same coin. Every team member writes code, designs and runs experiments, and interprets results.
Experience (or desire to be) working in multi-disciplinary teams.

What’s in it for you

Salary: Competitive
Hybrid schedule: We offer a flexible working schedule. #LI-HYBRID
Equity package: Generous equity scheme - everyone gets to be an owner of Robin AI!
Annual leave: 25 days PTO, in addition to the bank holidays observed in the UK.
Health and wellbeing: Comprehensive health insurance, mental healthcare, gym discounts and cycle to work scheme.
Growth opportunities: We prioritise promotions for high performers and help you to progress your career.

What’s it like working at Robin AI?

Our culture and values attract people who are creative, resourceful, and share our passion for excellence. At Robin, you're encouraged to push yourself and empowered to take risks. We support each other to think big, try new ideas, and navigate uncertainty. Whether you're at our headquarters or one of our worldwide offices, you'll find a world of opportunities to grow, thrive, and make a meaningful impact. See what life is like at Robin.

Diversity, Equity and Inclusion at Robin AI

We are committed to building one of the most diverse technology companies in the world. As of 2024, more than 30% of our employees come from ethnic minority backgrounds, and 51% of roles are held by women. We know that transforming the legal industry requires diverse perspectives, so we're creating an environment where innovation thrives through inclusion.

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CEO of Robin AI

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What You Should Know About Staff Applied Scientist - Search, Robin AI

At Robin AI, we're on an incredible journey to revolutionize the legal industry, and we're looking for a talented Staff Applied Scientist - Search to join our team in London. You’ll become part of a pioneering environment where advanced machine learning and data science meet legal applications. In this role, you will be at the forefront of innovative research that tackles significant challenges in the legal domain. You will design, lead, and experiment with cutting-edge methodologies that enhance our understanding of large language models (LLMs) and how they can be optimized for personalization in legal services. Your daily tasks will include developing algorithms for enhancing the transparency of these models and working collaboratively with other talented researchers to communicate your findings effectively. Not only will you contribute to the advancement of AI technology, but you'll also have the chance to mentor junior researchers, all while making a lasting impact within one of the fastest-growing tech spaces. If you have a Ph.D. in a relevant field, coupled with a passion for machine learning and a knack for teamwork, you might just be the perfect fit for our collaborative culture at Robin AI. Join us as we drive meaningful change in the legal landscape, providing smarter, more personalized solutions for businesses worldwide.

Frequently Asked Questions (FAQs) for Staff Applied Scientist - Search Role at Robin AI

What are the key responsibilities of a Staff Applied Scientist - Search at Robin AI?

As a Staff Applied Scientist - Search at Robin AI, you'll lead significant research in understanding and optimizing large language models for the legal industry. Your responsibilities will include designing algorithms for enhancing model transparency, conducting experiments, developing methodologies for interacting with new data architectures, and building evaluation sets to identify model failure modes.