Ground every answer in facts on this page and the original listing. We never invent Glassdoor-style reviews or salaries that are not in our data.
Prepare probability, ML fundamentals, coding, and research communication. Be ready to discuss past experiments, failure modes, and how methods map to trading/systems constraints. Skim public technical culture signals (e.g. Incremental discussions) rather than inventing firm interview scripts.
Fit is strong if you like research rigor inside a competitive, engineering-heavy trading environment in London. Less fit if you need remote work or a pure product-ML product org without markets intensity.
As a Machine Learning Researcher at Jane Street in London, expect research-to-production work: framing market or systems problems, prototyping models, rigorous evaluation, and pairing with traders and engineers. Days mix literature, experiments, code review, and clear write-ups—aligned with a quant firm under AI scrutiny, not a pure academic lab.
Focus on probability, optimization, ML systems, and production code. The supplied CERTS list targets Registered Nurses (Saylor Health Sciences free; Coursera audit healthcare free to learn)—not ML-specific; treat those only if you need adjacent free learning platforms, not as role credentials.
Listed remote flag is 0—plan for London on-site.
News highlights AI visibility and culture/hiring chatter; HN covers technical projects like Incremental—not employee review aggregates.
No—the CERTS family is Registered Nurses (free Saylor and free-to-learn Coursera audit tracks). Use them only as free course hosts, not as required nursing credentials for this title.
Website: janestreet.com
Public cache only — not an employee review.
MACHINE LEARNING RESEARCHER at Janestreet in London, Full Time, No Remote Position Summary We’re seeking smart and curious individuals from industry and academia to join our growing Machine Learning team and drive our cutting-edge work. As a key member of this dynamic group, you will build deep learning models that power our trading strategies, supported by our rapidly expanding computing cluster with tens of thousands of high-end GPUs. Trading presents unique challenges—large models and nonstationary datasets in a competitive multi-agent environment—that force us to explore novel techniques. Day-to-Day Responsibilities You will train models for the next generation of deep learning-based trading strategies, contributing to the fundamental understanding we need to tackle new markets and situations. Your role involves working closely with researchers, engineers, and traders who sit just a few feet away from each other. Depending on the day, you might be diving into market data, tuning hyperparameters, debugging distributed training performance, or studying how your models perform in production. Build deep learning models for trading strategies Work with a rapidly growing computing cluster of tens of thousands of high-end GPUs Collaborate closely with researchers, engineers, and traders to train models, architect systems, and run trading strategies Explore market data, tune hyperparameters, debug distributed training performance, and study model behavior in production Qualifications We are looking for individuals who have a deep understanding of the machine learning landscape and experience with various approaches—drawn from LLMs, image models, RL agents, recommendation systems, or classical ML methods. You should be comfortable working in a fast-paced environment where you will be hiring new colleagues, attending conferences, and teaching techniques to teammates—all of which we consider real and impactful parts of the job. About Janestreet At Jane Street, we value curiosity and innovation. Our team is dedicated to pushing the boundaries of machine learning in finance, and we are committed to fostering a collaborative environment where ideas can flourish. Join us if you’re ready for a challenge and eager to contribute to the future of ML in trading. Ready to Apply? If you’ve never thought about a career in finance, you’re in good company.…
Generated for personal interview prep · 2026-08-03 UTC · getajob.ai