ML Scientist

Reposted 17 Days Ago
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City of London, London, England
In-Office
Mid level
Artificial Intelligence • Machine Learning • Natural Language Processing • Software • Analytics • Generative AI
The Role
Develop natural language understanding systems for capital markets, prototype novel methods, and design ML tasks for customer use.
Summary Generated by Built In

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.

As an experienced ML expert in the Data Science team, you will part of an innovative team of machine learning researchers, engineers, and domain experts. Here you will have the opportunity to prototype novel methods to analyse linguistic corpora that are not widely available, and to contribute to the development of cutting-edge models, while learning about specialised topics in the financial domain. You will gain experience of company and culture creation in still early stage of our start-up journey.

The Role:

  • Provide scientific depth in a data science team including ML and NLP scientists and linguists.
  • Design new ML tasks from raw data through new ML methods to finalised products that will be used by our customers.
  • 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 and complete ML pipelines.
  • Some problems you might work on:
    • Automated flagging of cases where model predictions might be incorrect.
    • Building more efficient data annotation strategies to fine-tune an LLM.

Requirements:

  • MSc with enough research experience (industry or academia) or PhD in a relevant subject.
  • 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. Knowledge of LLMs is a must have.
  • Interest for text and language tasks.
  • Experience with designing and orchestrating ML experiments at scale, e.g. training with multiple GPUs

Nice to have:

  • 1+ years of industry experience in deep learning.
  • Knowledge of foreign languages.
  • Experience with UNIX and cloud computing.

You:

  • Have good communication and collaboration skills.
  • Have good analytical and organisational skills.
  • Cooperate well with other professionals of different backgrounds.
  • See the inherent value in a respectful and diverse workplace.
  • Benefits Highly skilled team, flat hierarchy, and opportunities for mentorship.
  • Ability to heavily influence platform and culture from an early phase.

What we provide:

  • Flexible working, central London location, company share option scheme.
  • Budget/time for books, training and attending conferences/hackathons.

Top Skills

Cloud Computing
Deep Learning
Llms
Ml
Nlp
Unix
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The Company
HQ: London
39 Employees
Year Founded: 2019

What We Do

We are developing natural language understanding systems for capital markets. Our premise is simple: markets are conversations and we aim to help its participants have better and more efficient conversations.

Through our partnerships with global financial institutions we have access to datasets that have not been made available in the past. This allows us to create language models that are uniquely suited to capital markets while advancing the state-of-the-art.

Our platform has been developed to ensure robust and safe delivery of large language models into the financial enterprise. The resulting applications enhance analytics, workflow automation and increased observability.

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