AI Engineering Internship
Location: Remote
Duration: Rolling Basis
Company: A.M. Dirac, Quantitative Proprietary Trading Firm
About A.M. Dirac
A.M. Dirac is an early-stage, quantitative proprietary trading firm specializing in applying advanced mathematical models and machine learning techniques to financial markets. We focus on developing high-performance strategies across futures, foreign exchange, and equities markets. Our team of quantitative researchers and traders work in a collaborative and fast-paced environment, leveraging cutting-edge technology to solve some of the most complex problems in quantitative finance.
Position Overview
We are seeking a highly motivated AI Engineering Intern to join our team. This is an exciting opportunity to apply your machine learning expertise to high-dimensional financial and alternative data sets to create novel alphas, improve portfolio optimization, and enhance risk modeling. As an intern, you will work closely with experienced researchers and engineers, contributing to real-world trading strategies and financial models.
Key Responsibilities
Model Application: Apply existing machine learning models to large-scale financial datasets, including futures, foreign exchange, and equities intraday and tick data.
Alpha Generation: Work on developing predictive models for alpha generation using applicable machine learning techniques.
Portfolio Optimization: Contribute to development of portfolio construction and optimization models.
Risk Modeling: Assist in the development and improvement of risk management models to assess and mitigate risk across multiple asset classes.
Data Exploration & Preprocessing: Conduct data analysis and preprocessing on high-dimensional, alternative datasets (such as sentiment analysis, social media, macroeconomic data) to uncover potential market signals.
Collaboration: Work alongside a team of quantitative researchers and engineers to iterate and improve machine learning models, ensuring their robustness and real-world applicability.
Qualifications
Required:
Ph.D. Candidate: 3rd year or higher Ph.D. candidate in Machine Learning, Computer Science, Mathematics, Finance, or a related quantitative field. (Exceptions may be made for extraordinary candidates with significant experience or technical skills.)
Programming Skills: Strong experience in Python and Jupyter for data analysis, model development, and experimentation. Familiarity with SOTA models in deep learning and LLM a plus.
Machine Learning Expertise: Proficiency in applying machine learning algorithms, including supervised and unsupervised learning, neural networks, and time series models, to complex datasets.
Analytical Mindset: Strong quantitative and analytical skills, with a passion for solving complex problems and uncovering patterns in large datasets.
Preferred:
Proficiency in Other Programming Languages: Familiarity with other languages such as C++, Go, or Rust is a plus, though not required.
Experience with Big Data Tools: Familiarity with data processing and storage systems (e.g., Hadoop, Spark, SQL, etc.) is a plus.
Experience with Financial Data: Understanding of financial markets, including equities, futures, and foreign exchange, and how they can be modeled and analyzed using machine learning techniques.
Experience in Trading or Financial Engineering: Previous experience in a trading, financial, or research role is highly desirable but not essential.
Strong Communication Skills: Ability to clearly present technical findings to both technical and non-technical stakeholders.
Why A.M. Dirac:
Real-world Impact: Contribute to the development of trading strategies and risk models used in real-world markets.
Collaborative Environment: Engage with a diverse and highly talented team of researchers, data scientists, and engineers.
Cutting-Edge Technology: Gain experience with state-of-the-art tools and technologies in both machine learning and finance.
Compensation: Competitive compensation based on experience and location, with potential for future opportunities within the firm.
Application Process:
Interested candidates should submit:
Resume/CV highlighting relevant academic and technical experience.
Sample Code or Project (optional) that demonstrates your experience with machine learning or quantitative finance problems. GitHub repositories are welcome.
A.M. Dirac is committed to building a diverse and inclusive team. We encourage candidates from all backgrounds to apply.
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