Machine Learning Researcher - Intern

JOB RESPONSIBILITIES A highly collaborative, fast-growing team, Point72 Internal Alpha Capture (IAC) is developing scalable quantitative equity trading signals that leverage rigorous research, state-of-the-art machine learning methods, a broad range of public and proprietary data sources, and unparalleled computing power. We are looking for exceptional students to join us as quantitative researcher interns for Fall 2026, Spring 2027 and Summer 2027. Our interns will work closely with our team, receive comprehensive, in-depth training, and help develop novel signals that may have real impact. You will gain exposure to a variety of activities, which may include: Conducting full pipeline signal research, from ideation, via implementation and backtesting, to eventual application. Adapting and extending existing results in the broad field of machine learning; conducting novel research as needed to potentially develop new signals that may enhance portfolio returns. Analyzing very large data sets to extract features useful for predictive models. Providing research assistance to the team. Helping improve the team’s research infrastructure. DESIRABLE CANDIDATES Masters or PhD candidates in machine learning, computer science, finance, mathematics, or other quantitative disciplines. Demonstrated ability to conduct independent research utilizing machine learning over large, possibly noisy, data sets. Strong analytical and quantitative skills, and detail-oriented mindset. Proficient in Python or C++, and Linux environment. Excellent written and verbal communication skills, willing to proactively engage other team members in helping to foster a highly collaborative, team-oriented research environment. Willing to take ownership of his/her work, working both independently and within a collaborative team. Prior experience in the financial services industry is not required. Commitment to the highest ethical standards. The annual base salary range is $120000.00-$180000.00&n

Role/Responsibilities: We are seeking a quantitative researcher to join an NLP quant team within Point72. We believe the significant advances in NLP methods show promise for finance. We develop and launch end-to-end signals, from data processing to performance testing. The ideal candidate will have strong machine learning, data science and software engineering skills, some experience with modern NLP, and a curiosity about finance and trading. Responsibilities may include: Using NLP to construct features from varied datasets Formulating research hypotheses to derive alpha Building and testing the performance of trading signals based on NLP and financial features Launching identified signals into production Requirements: Bachelor’s, Master’s or PhD degree in computer science or other quantitative discipline Proficient in Python and general software engineering principles (github, testing, dev workflow) Strong understanding of machine learning and statistics Prior research experience preferred Experience with deep learning, specifically NLP (PyTorch, HuggingFace, LLMs, etc.) preferred Data science stack familiarity preferred An interest in financial markets Great attitude A collaborative mindset Commitment to the highest ethical standards About Point72: Point72 Asset Management is a global firm led by Steven Cohen that invests in multiple asset classes and strategies worldwide. Resting on more than a quarter-century of investing experience, we seek to be the industry’s premier asset manager through delivering superior risk-adjusted returns, adhering to the highest ethical standards, and offering the greatest opportunities to the industry’s brightest talent. We’re inventing the future of finance by revolutionizing how we develop our people and how we use data to shape our thinking. For more information, visit www.Point72.com/working-here.

ROLE/RESPONSIBILTIES: We are seeking a quant research intern to join an NLP quant team within Point72. We believe the significant advances in NLP methods show promise for finance. We develop and launch end-to-end signals, from data processing to performance testing. The ideal candidate will have strong machine learning, data science and software engineering skills, some experience with modern NLP, and a curiosity about finance and trading. Responsibilities may include: Using NLP to construct features from varied datasets Formulating research hypotheses to derive alpha Building and testing the performance of trading signals based on NLP and financial features Launching identified signals into production REQUIREMENTS: Bachelor’s, Master’s or PhD candidate in computer science or other quantitative discipline Proficient in Python and general software engineering principles (github, testing, dev workflow) Strong understanding of machine learning and statistics Prior research experience preferred Experience with deep learning, specifically NLP (PyTorch, HuggingFace, LLMs, etc.) preferred Data science stack familiarity preferred An interest in financial markets Great attitude A collaborative mindset Commitment to the highest ethical standards ABOUT POINT72: Point72 is a leading global alternative investment firm led by Steven A. Cohen. Building on more than 30 years of investing experience, Point72 seeks to deliver superior returns for its investors through fundamental and systematic investing strategies across asset classes and geographies. We aim to attract and retain the industry’s brightest talent by cultivating an investor-led culture and committing to our people’s long-term growth.

ABOUT CUBIST: Cubist Systematic Strategies, an affiliate of Point72, deploys systematic, computer-driven trading strategies across multiple liquid asset classes, including equities, futures and foreign exchange. The core of our effort is rigorous research into a wide range of market anomalies, fueled by our unparalleled access to a wide range of publicly available data sources. RESPONSIBILITIES: Building components for both live trading and simulation Refining and increasing automation and robustness of the research infrastructure including alpha estimation, risk modeling, and backtesting components Building tools for signal blending, simulation, portfolio construction, the research framework, and dashboards Maintaining and updating the platform, ensuring its stability, robustness, and security Developing robust data checking and storage procedures Troubleshooting and resolving any systems related issues and handle the release of code fixes and enhancements REQUIREMENTS: Bachelor’s degree or higher in computer science or other STEM discipline Advanced proficiency in Python and its ecosystem (numpy, pandas, polars, scikit-learn), with an understanding of Python and library internals Experience contributing to core Python numerical libraries is a huge plus (numpy, tensorflow, torch, jax) Proficiency with Linux Hands-on experience with software architecture and engineering best practices (testing, CI/CD, monitoring, profiling, version control) Strong quantitative and analytical skills; command of linear algebra, statistics, and machine learning would be helpful Proficiency with C/C++ is a plus Experience with designing and implementing trading systems is a plus Commitment to the highest ethical standards

About Cubist Cubist Systematic Strategies, an affiliate of Point72, deploys systematic, computer-driven trading strategies across multiple liquid asset classes, including equities, futures and foreign exchange. The core of our effort is rigorous research into a wide range of market anomalies, fueled by our unparalleled access to a wide range of publicly available data sources. Role/Responsibilities: We are seeking a quantitative researcher for the Cubist Machine Learning Research group with experience in machine learning, especially recent deep learning and natural language processing technology. Researchers will use a rigorous scientific method to develop sophisticated trading models and shape our insights into how the markets will behave. Successful researchers manage all aspects of the research process including data ingestion and processing, data analysis, methodology selection, implementation and testing, prototyping, and performance evaluation. Researchers will be introduced to industry standard datasets, including understanding which data may be relevant to a certain model or financial problem; how to collect, parse, and clean the data; how to incorporate the data into innovative functional models; how to construct and develop features from raw data; and how to estimate effectiveness of such features. Researchers will also be provided with the opportunity to implement the full breadth of their knowledge and training to actively participate in all stages of research & development of financial models through use of machine learning. Based on experience from working with existing industry-standard models and algorithms, researchers will learn how to construct their own models in order to solve complex financial problems and enhance data prediction capabilities within the financial services industry. Requirements: PhD or PhD candidate in machine learning, computer science, statistics, or a related field Experience with sequential modeling and time

About the Team: A well-established quantitative portfolio management team at Point72 is looking for an experienced quantitative professional to develop and trade systematic macro strategies, with a focus on market microstructure. The candidate will be given the resources and support to drive the build out and expansion of the quantitative macro business. Role/Responsibilities: Perform rigorous and innovative research to develop systematic signals for global macro (futures, FX, etc.) markets, with a focus on market microstructure signals Perform feature engineering with order book tick data at intraday to daily horizons Perform feature combination using various modeling techniques ranging from linear to machine learning models Participate in the research pipeline end-to-end, including signal idea generation, data processing, modeling, strategy backtesting, and production implementation Help drive the growth of the investment process and research capabilities of the team Work in a team of highly qualified and motivated individuals with access to a cutting-edge research and trading infrastructure and clean datasets Assist in building, maintenance, and continual improvement of production and trading environments Requirements: MS or PhD in physics, engineering, statistics, applied math, quantitative finance, or other quantitative fields with a strong foundation in statistics 4+ years of experience in quantitative research, building statistical models for intraday to daily trading, as part of a successful proprietary trading team with a track record Knowledge of market microstructure for futures and/or FX Prior experience with tick data based feature generation, modelling, and monetization Demonstrated proficiency in Python, R, or C/C++. Familiarly with data science toolkits, such as scikit-learn, Pandas Collaborative mindset with strong independent research abilities Commitment to the highest ethical standards