Jane Street

Jane Street is seeking a Machine Learning Researcher to work on projects combining novel ML ideas with systematic trading strategies. The role involves end-to-end studies, exploring new modeling paradigms, and applying state-of-the-art techniques to challenging problems in finance, utilizing large-scale data and computing resources. The position is for a PhD student or postdoc with empirical ML research experience, strong logical and mathematical thinking, and Python implementation skills.

Machine Learning Researcher to build deep learning models for trading strategies, leveraging large-scale GPU clusters and novel techniques to address challenges in nonstationary datasets and multi-agent environments. The role involves training next-generation models, understanding new markets, and contributing to hiring and knowledge sharing.

Machine Learning Researcher at Jane Street to build deep learning models for trading strategies, leveraging large-scale GPU clusters and tackling challenges like large models, nonstationary datasets, and multi-agent environments. The role involves training next-generation models, developing fundamental understanding for new markets, and contributing to hiring and knowledge sharing.

Machine Learning Researcher to build deep learning models for trading strategies, supported by a large GPU cluster. The role involves tackling challenges like large models, nonstationary datasets, and multi-agent environments, requiring novel techniques. Researchers collaborate closely with engineers and traders, diving into market data, hyperparameter tuning, distributed training, and model behavior in production. The position requires in-depth knowledge of ML, including LLMs, image models, RL agents, recommendation systems, and classical ML, to shape the future of ML at Jane Street. Responsibilities include training next-generation models, building fundamental understanding for new markets, hiring, attending conferences, and teaching teammates.

Machine Learning Performance Engineer role focused on optimizing the performance of ML models for both training and inference. Requires deep low-level systems programming and GPU knowledge, debugging skills, and experience with ML libraries and distributed training.

Machine Learning Researcher to build deep learning models for trading strategies, supported by a large GPU cluster. The role involves training models, architecting systems, and studying model behavior in production, with a focus on novel techniques for challenges like large models, nonstationary datasets, and multi-agent environments. Responsibilities include hiring, attending conferences, and teaching.

Machine Learning Researcher to build deep learning models for trading strategies, leveraging a large GPU cluster and tackling challenges like large models, nonstationary datasets, and multi-agent environments. The role involves training models, architecting systems, and researching novel techniques across various ML domains (LLMs, image models, RL agents, recommendation systems, classical ML). Responsibilities include training next-gen models, building fundamental understanding, hiring, attending conferences, and teaching teammates.

Research role focused on building deep learning models for trading strategies, leveraging a large GPU cluster. The role involves training models, architecting systems, and understanding market data in a competitive multi-agent environment, pushing for novel techniques across various ML domains like LLMs, image models, RL agents, and recommendation systems.

Seeking an engineer with low-level systems programming and optimization experience to join the ML team, focusing on optimizing the performance of ML models for both training and inference in a real-time trading environment. This role requires a deep understanding of GPU architecture, networking, and distributed systems to ensure efficient large-scale training and low-latency, high-throughput inference.

Machine Learning Performance Engineer at Jane Street, focusing on optimizing the performance of ML models for both training and inference. This role requires deep expertise in low-level systems programming, GPU optimization, and a whole-systems approach to performance, including storage and networking, within a high-frequency trading environment.

Research role focused on applied research in weather prediction, embedded within a commodities trading desk. Requires strong programming skills (Python), expertise in atmospheric science topics, and experience with AI models. The role involves challenging the limits of atmospheric predictability and exploring new research directions.

Applied research role on a weather team, embedded within a commodities trading desk, focusing on atmospheric predictability challenges. Requires strong Python programming and expertise in atmospheric science topics, with AI model experience preferred.

Quantitative Researcher role at Jane Street focusing on identifying market signals, analyzing data, building and testing models, and creating trading strategies. The role involves close collaboration with experienced researchers, working with large datasets and GPU clusters, and applying various statistical and ML techniques. Interns will work on projects, learn about experiment design, dataset generation, time series analysis, feature engineering, and model building for financial datasets, complemented by classes on markets and trading.

Jane Street is seeking Quantitative Researchers to build models and systems for pricing and trading financial instruments. The role involves working with large datasets and advanced computing infrastructure, applying various statistical and ML techniques, and collaborating closely with engineers and traders. The focus is on developing actionable strategies based on data analysis and model building.

Quantitative Researchers at Jane Street build models, strategies, and systems for pricing and trading financial instruments. The role involves working with experienced researchers on experiment design, dataset generation, time series analysis, feature engineering, and model building for financial datasets. Researchers collaborate closely with engineers and traders, utilizing large-scale data and computing resources. The role is open to various ML techniques, from linear models to deep learning, and emphasizes curiosity, logical thinking, strong programming skills (Python), and collaboration.

Quantitative Researchers at Jane Street build models, strategies, and systems for pricing and trading financial instruments. The role involves working with large datasets and advanced computing infrastructure, applying various statistical and ML techniques, and collaborating closely with engineers and traders. Success requires a curious, logical, and programming-proficient individual who can adapt findings into actionable strategies.

Jane Street is seeking an experienced Machine Learning Educator to develop and maintain their ML educational programs. This role involves teaching general ML, AI assistants, internal training stacks, and performance reasoning. The educator will also write code for ML research, mentor other teachers, and develop curricula. Experience teaching ML in a university or industry setting is required.

Quantitative Researchers at Jane Street build models, strategies, and systems for pricing and trading financial instruments. The role involves applying expertise in experiment design, dataset generation, time series analysis, feature engineering, and model building to financial datasets. Researchers collaborate with engineers and traders, working with large datasets and GPU clusters. The position is open to various statistical and machine learning techniques, including deep learning, and requires strong programming skills in Python and a curious, collaborative mindset.

Machine Learning Engineer at Jane Street to drive the direction of their ML platform, focusing on building and maintaining training and inference infrastructure, enhancing research workflows, and applying ML to financial trading. Requires strong mathematical foundations and experience with ML frameworks.

Jane Street is seeking a Machine Learning Engineer to join their ML team and drive the direction of their ML platform. The role involves building and maintaining training and inference infrastructure, enhancing research workflows, and applying ML to financial trading. Requires a strong mathematical background and expertise in ML frameworks.

Jane Street is seeking a Machine Learning Engineer with a strong mathematical background to join their ML team and drive the direction of their ML platform. The role involves applying various ML techniques to aid decision-making in their trading environment, enhancing research workflows, and building/maintaining training and inference infrastructure to move concepts to production.

Quantitative Researchers at Jane Street build models, strategies, and systems for pricing and trading financial instruments. The role involves applying expertise in experiment design, dataset generation, time series analysis, feature engineering, and model building to financial datasets. Researchers collaborate closely with engineers and traders, working with large datasets and GPU clusters. The role is open to various statistical and machine learning techniques, emphasizing curiosity, problem-solving, and adaptability.

Jane Street is seeking Quantitative Researchers to build models, strategies, and systems for pricing and trading financial instruments. The role involves applying expertise in experiment design, dataset generation, time series analysis, feature engineering, and model building to financial datasets. Researchers will collaborate with engineers and traders, working with large datasets and a significant GPU cluster. The position is open to various statistical and machine learning techniques, emphasizing curiosity and the ability to integrate findings into actionable trading strategies.