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- Tasks: Develop cutting-edge tools for AI/ML in investment management and collaborate on innovative research.
- Company: Two Sigma, a leading quantitative investment firm with a focus on technology and data science.
- Benefits: Competitive salary, health benefits, and opportunities for professional growth in a dynamic environment.
- Why this job: Join a team that tackles complex economic problems using advanced technology and creative solutions.
- Qualifications: BS in Computer Science or related field; experience in quantitative software and machine learning preferred.
- Other info: Exciting opportunity to work with top-tier engineers and researchers in a collaborative setting.
The predicted salary is between 60000 - 80000 £ per year.
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 Engineer to contribute to our Learning Engineering efforts.
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.
- Model development: prototype, test, and implement models utilized across Two Sigma Quantitative systems, design new architectures and/or develop systems that power research and trading activities.
- Tooling: develop and scale the tools, frameworks, and libraries used by our teams to conduct research and build models—improving performance optimization and scalability of these capabilities.
- 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.
Quantitative Software Engineer, Learning Engineering in London employer: Two Sigma
Contact Detail:
Two Sigma Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Quantitative Software Engineer, Learning Engineering in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to quantitative finance or machine learning. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for technical interviews by brushing up on your coding skills and understanding algorithms. Practice common interview questions and work on mock interviews with friends or online platforms to build confidence.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team at StudySmarter.
We think you need these skills to ace Quantitative Software Engineer, Learning Engineering in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the Quantitative Software Engineer role. Highlight your experience in quantitative finance, machine learning, and any relevant technologies like NumPy or SciPy.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about the role and how your background makes you a great fit. Be specific about your experiences and how they relate to our mission at Two Sigma.
Showcase Your Projects: If you've worked on any relevant projects, whether personal or professional, make sure to include them. We love seeing practical applications of your skills, especially in areas like model development or tooling.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows us you’re serious about joining our team!
How to prepare for a job interview at Two Sigma
✨Know Your Tech Stack
Make sure you’re familiar with the technologies mentioned in the job description, like NumPy, SciPy, and scikit-learn. Brush up on your Python skills and be ready to discuss how you've used these tools in past projects.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific examples where you've tackled complex problems using quantitative methods. Think about how you approached model development or optimised performance in previous roles, as this will demonstrate your ability to contribute to Two Sigma's innovative environment.
✨Understand the Business Context
While financial experience isn’t a must, having a grasp of how quantitative finance works can set you apart. Research Two Sigma’s approach to investment management and be ready to discuss how your skills can help advance their AI/ML capabilities.
✨Ask Insightful Questions
Prepare thoughtful questions that show your interest in the role and the company. Inquire about the current challenges the Learning Engineering team faces or how they envision the future of AI/ML in their investment strategies. This shows you're not just looking for a job, but are genuinely interested in contributing to their mission.