At Sense Street, we are developing natural language understanding systems for capital markets. Our premise is simple: markets are conversations, and we aim to help investment banks and asset managers have better, more efficient conversations. Through our partnerships with global banks, we have access to datasets that have not been made available in the past. This allows us to create language models uniquely suited to capital markets while advancing the state-of-the-art. We are a venture backed company founded by professionals with experience spanning machine learning, trading, and quantitative research.

Please read the information in this job post thoroughly to understand exactly what is expected of potential candidates.As an experienced ML Engineer in the Data Science team, you will part of an innovative team of machine learning scientists, engineers, domain experts, and annotators. Here you will have the opportunity to prototype novel methods for dialogue data that are not widely available, and to contribute to the development of cutting-edge models, while productionising product in the financial domain. You will gain experience of company and culture creation in the scaling stages of our start-up journey.

The Role:

Provide scientific depth in a data science team including ML and NLU scientists and linguists.

Design new ML tasks from raw data through new ML methods to finalised products that will be used by our customers.

Collaborate with product, annotation team, and engineers to drive process around ML pipeline.

Drive innovation on tasks that are in a specialised domain and do not conform to standard NLP tasks.

Think creatively and innovatively to produce effective models.

Requirements:

Demonstrable ML experience (either through MSc or PhD or relevant industry experience)

Good programming skills, especially with Python and familiarity with Pandas, PyTorch

Proven track-record of end-to-end design for ML systems

Experience with Linux systems as everything we do is basically on Linux VMs.

Extensive background in using and developing deep learning methods.

Substantial understanding of deep learning methods and breadth of knowledge of different models and their potential applications.

Interest for text and language tasks.

Nice to have:

Knowledge of foreign languages.

Familiarity with Pydantic, Streamlit.

Familiarity with Elasticsearch

Have basic understanding of LLM architectures, multi-gpu training, and evaluation.

You:

Communicate and collaborate effectively across multiple teams.

Are an analytical problem-solver.

Cooperate well with other professionals of different backgrounds.

See the inherent value in a respectful and diverse workplace.

Ability to read and understand latest research, and adapt these ideas into our work

Benefits

Highly skilled team, flat hierarchy, and opportunities for mentorship.

Ability to heavily influence platform and culture in a scaling company.

Flexible working, central London location, company share option scheme.

Budget/time for books, training and attending conferences/hackathons.

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