Data Product Engineer

Data Product Engineer

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.

This is a new and evolving role, ideal for a data or software engineer who wants to work at the intersection of data engineering, ML infrastructure, and product integration. We don't expect you to have experience with all of these-this role is a great opportunity for someone who wants to grow into and shape the intersection of AI, data, and product development.

You'll collaborate closely with ML engineers, data scientists, and product & UX design to bring AI models into production, develop robust data infrastructure, and contribute to how data is accessed and used-whether through APIs, internal tools, or analytics interfaces. If you enjoy building scalable data solutions while thinking about how they fit into the bigger picture, this role offers an exciting challenge.

The Role

• Build and maintain scalable data pipelines to support AI/ML workloads.
• Develop APIs and backend services that integrate AI models into production systems.
• Work with ML engineers to deploy and optimize AI models in real-world applications.
• Contribute to architecture decisions that shape our AI/ML infrastructure.
• Think about how data is accessed and used, including internal tools, dashboards, and analytics interfaces.
• Implement monitoring, observability, and data reliability best practices.

Requirements

• Strong software or data engineering background (Python, SQL, cloud platforms).
• Experience with data pipelines, backend systems, and APIs (ETL/ELT, microservices).
• Familiarity with distributed systems, cloud environments (AWS/GCP/Azure), and CI/CD.
• Interest in working closely with ML engineers and AI-powered systems.
• Willingness to think about data accessibility and usability, not just infrastructure.
• Excited to grow into the role and help define it as it evolves.

Nice to Have

• Experience working with ML models and their deployment.
• Familiarity with ML infrastructure, feature stores, and MLOps best practices.
• Exposure to deep learning frameworks (PyTorch, TensorFlow).
• Experience with building internal tools, dashboards, or lightweight front-end components (e.g., Streamlit, Dash, or BI tools like Looker, Tableau) or JavaScript frameworks (React, etc).

The Work Environment

• Flexible working, central London location.
• Highly skilled team, flat hierarchy, and opportunities for mentorship.
• Ability to heavily influence platform and culture in a scaling company.
• Budget/time for books, training and attending conferences/hackathons.
• Opportunity to work with cutting-edge AI technologies and unique datasets.
• Regular knowledge sharing sessions and internal tech talks.

Company Benefits

• Flexi Working.
• Pension.
• Laptop.
• Hybrid working - home/office.
• Share Options Scheme.
• Private Healthcare.