At a Glance
- Tasks: Design and build high-performance systems for systematic trading using cutting-edge technology.
- Company: Join a pioneering team at the forefront of systematic trading innovation.
- Benefits: Competitive salary, dynamic work environment, and opportunities for professional growth.
- Other info: Collaborate with researchers and traders in a fast-paced, innovative setting.
- Why this job: Make a real impact in the world of AI-driven trading and quantitative research.
- Qualifications: Bachelor’s or higher in a technical field with strong C++ and Python skills.
The predicted salary is between 63000 - 77000 £ per year.
Description
Join a pioneering team at the forefront of systematic trading innovation.
The Quantitative Trading & Research (QTR) group is responsible for systematic trading across FX, Rates, Commodities, Credit, Equity and a wide range of markets.
Within QTR, AI Market Lab brings together quantitative research, modern artificial intelligence, market microstructure, and high-performance engineering to develop the next generation of electronic trading capabilities.
Our work spans signal research, pricing, market making, execution, portfolio construction, risk management, and the production systems that support them.
Job Summary
As a Quantitative Trading & Research – Quantitative developer in the AI Market Lab, you will work at the boundary of quantitative research, low-latency engineering, and ML infrastructure, creating reliable platforms that shorten the path from raw market data and research prototypes to monitored, resilient production strategies.
We are seeking strong talent to build the research and production technology behind AI-driven systematic trading.
This role is ideal for engineers who enjoy turning ambiguous research requirements into clean interfaces, fast systems, and reproducible workflows—without losing sight of trading realities like latency, determinism, and operational risk.
- Job Responsibilities
- Design and build high-performance market-data, feature-computation, backtesting, simulation, model-serving, execution, and monitoring components for systematic trading.
- Develop reliable low-latency C++ services and APIs that integrate quantitative models with real-time market data, pricing, risk controls, and order-management systems.
- Build scalable data and research pipelines that support granular historical data, reproducible experiments, distributed computation, and rapid strategy iteration.
- Optimize critical paths for throughput, tail latency, memory efficiency, resilience, and deterministic behavior; use profiling and measurement to guide engineering decisions.
- Productionize machine-learning models, including training workflows, model versioning, real-time inference, deployment automation, observability, and rollback controls.
- Partner with researchers and traders to translate strategy requirements into robust software, improve research-to-production consistency, and support live systems.
- Required Qualifications, Capabilities, and Skills
- Bachelor’s, Master’s, or Ph D in computer science, engineering, mathematics, or a related technical discipline (or equivalent professional experience).
- Relevant professional experience in software engineering, quantitative development, low-latency systems, or ML infrastructure.
- Strong modern C++ skills in data structures, concurrency, memory management, performance profiling, and production debugging.
- Proficiency in Python and experience building software for quantitative researchers or other data-intensive applications.
- Solid understanding of distributed systems, testing, software design, reliability, and operating production services end-to-end.
- Evidence of owning performance-critical systems from design, deployment, monitoring to incident resolution.
- Preferred Qualifications, Capabilities, and Skills
- Experience with electronic trading architecture: exchange connectivity, market-data normalization, order management, pre-trade risk, or execution systems.
- Knowledge of Linux performance engineering: kernel/network tuning, lock-free programming, hardware-aware optimization, or FPGA-adjacent systems.
- Experience with ML/data tooling such as Py Torch, JAX, CUDA, GPU clusters, Ray, Kafka, Kubernetes, Spark, or comparable technologies.
- Understanding of market microstructure, backtesting pitfalls, transaction costs, and the operational needs of live quantitative strategies.
- Experience in environments operating real-time systems (hedge fund, proprietary trading firm, market maker, exchange, or financial institution).
Quantitative Trading & Research - Quantitative Developer - Associate or Vice President in London employer: JPMorganChase
J.P. Morgan Europe Limited is an exceptional employer, offering a dynamic work culture that fosters collaboration and innovation in the heart of the financial sector. Employees benefit from comprehensive growth opportunities, competitive compensation, and a commitment to professional development, all while contributing to impactful consumer banking initiatives. Working here means being part of a prestigious institution that values insights and empowers its team members to drive meaningful change.
StudySmarter Expert Advice🤫
We think this is how you could land Quantitative Trading & Research - Quantitative Developer - Associate or Vice President in London
✨Join Local Tech Meetups
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✨Contribute to Open Source Projects
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We think you need these skills to ace Quantitative Trading & Research - Quantitative Developer - Associate or Vice President in London
Some tips for your application 🫡
Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at JPMorganChase.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at JPMorganChase and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!
How to prepare for a job interview at JPMorganChase
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
✨Know Your Tools and Frameworks
Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If JPMorganChase uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
✨Showcase Your Projects
Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.
