Quantitative Software Engineer, Learning Engineering
UK Visa Sponsorship Analytics
Occupation TypeProgrammers and software development professionals
Occupation Code Skill LevelHigher Skilled
Sponsorship Salary Threshold
£54,700
(£28.05 per hour)
Occupation rate applies
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Description
Position Summary
Two Sigma is a leading quantitative investment management and trading firm. The company applies a scientific approach to investing, combining cutting-edge technology, artificial intelligence, data science, and quantitative research with rigorous human inquiry to capitalize on market opportunities and deliver alpha for investors.
Our team of engineers, quantitative researchers and data scientists looks beyond the traditional to test hypotheses and develop creative solutions to some of the world’s most complex economic problems.
The Learning Engineering team’s mission is to create cutting-edge tools that advance AI/ML capabilities for our investment management business. Our work spans from large-scale model distributed training, LLM hosting and fine-tuning capabilities, to learning and scoring across a wide array of techniques.
We are seeking a Quantitative Software Engineering to contribute to our Learning Engineering efforts. Your goal will be to deliver world-class AI/ML capabilities and integrate new and evolving technologies into our internal ecosystem, advancing our investment management business.
You Will Take On The Following Responsibilities
Two Sigma is a leading quantitative investment management and trading firm. The company applies a scientific approach to investing, combining cutting-edge technology, artificial intelligence, data science, and quantitative research with rigorous human inquiry to capitalize on market opportunities and deliver alpha for investors.
Our team of engineers, quantitative researchers and data scientists looks beyond the traditional to test hypotheses and develop creative solutions to some of the world’s most complex economic problems.
The Learning Engineering team’s mission is to create cutting-edge tools that advance AI/ML capabilities for our investment management business. Our work spans from large-scale model distributed training, LLM hosting and fine-tuning capabilities, to learning and scoring across a wide array of techniques.
We are seeking a Quantitative Software Engineering to contribute to our Learning Engineering efforts. Your goal will be to deliver world-class AI/ML capabilities and integrate new and evolving technologies into our internal ecosystem, advancing our investment management business.
You Will Take On The Following Responsibilities
- Become an authority for the systems underpinning our research areas (ML, Finance, and/or quantitative algorithms) and help evolve these components
- Work closely with our research partners to conceptualize and iterate within new areas of research and development. Quantitative Engineers can have a diverse mandate including:
- Model development: prototyping, testing, and implementing models utilized across Two Sigma
- Quantitative systems: designing new architectures and/or developing systems that power research and trading activities at Two Sigma
- Quantitative tooling: developing and scaling the tools, frameworks, and libraries that are used by our teams to conduct research and build models - improving performance optimization and scalability of these capabilities
You Should Possess The Following Qualifications
- BS in Computer Science, Applied Mathematics, or related technical field
- Minimum 1 year of experience required; 3-10 years of experience preferred
- Professional experience building quantitative software across at least one of the following areas: quantitative finance, math/stats/numeric methods, and machine learning/deep learning
- Experience applying technologies and libraries such as NumPy, SciPy, or scikit-learn
- Experience with scientific computing and algorithm development
- Knowledge of scripting languages such as Python
- A background in building large-scale, real-time, and distributed applications is desired
- While we analyze the data-rich domain of finance, financial experience is not a requirement