Job Location: 100 Avenue of the Americas, New York, NY 10013

Note: Company “Hybrid” work attendance policy: In-office work attendance required at the aforementioned office address for collaboration days based on each team’s requirement; telecommuting / working from home is permissible for remainder of the same month.

Duties: Discover, ideate, research, develop, train, and evaluate advanced control policies and algorithms, such as reinforcement learning agents, to optimize portfolio construction, order execution, or to monetize various trading signals, such as equity returns forecasts, volatility predictions, etc; Backtest these control models on historical data by implementing them in high-performance simulation systems; Identify and research areas for improvement in these control models by productionalizing and monitoring them in low-latency trading systems to gather feedback based on their trading behavior in real-world global financial markets and exchanges (e.g. NYSE, NASDAQ, Shanghai Stock Exchange, etc.). Discover, ideate, research, develop, train, and evaluate advanced predictive models, such as deep neural networks, to forecast future price movements, market microstructures, or market impacts of various financial instruments ranging from equities to commodities; Backtest these predictive models on historical data by implementing them in high-performance simulation systems; Identify and research areas for improvement in these predictive models by productionalizing and monitoring them in low-latency trading systems to gather feedback based on their prediction behavior in real-world market data and environments. Discover, ideate, research, validate, and evaluate novel financial features by finding statistical patterns in large, complex, and noisy financial datasets. Formulate mathematical models of various aspects of the financial markets using the scientific method. Apply these features and insights to the development of various financial forecasts, trading tactics, risk managements, anomaly detections, etc.

Minimum education and experience required: Master’s degree or the equivalent in Computer Science, Mathematics, Statistics, Machine Learning, or related field. Position does not require specific years of experience but requires listed skills.

Skills required: Must have demonstrated knowledge with Python, Groovy, Linux, shell scripting, and Git. Must have demonstrated knowledge with PyTorch, TensorFlow, JAX, and distributed training and inference in multi-GPU/TPU and multi-node settings. Must have demonstrated knowledge with C++ and low-level deep learning systems, such as PyTorch/XLA, performance profilers, CUDA, JIT compilers, etc. Must have demonstrated knowledge with debugging large (>100k lines of code) multi-language (Python, C++, etc.) deep learning codebases. Must have demonstrated knowledge with time-series ML modeling, time-series data engineering, and distributed time series databases. Must have demonstrated knowledge with big data analytics and data science frameworks, including Pandas, NumPy, scikit-learn, Matplotlib, Plotly, Jupyter/JupyterLab, etc. Must have demonstrated knowledge and mathematical maturity in linear algebra, multivariable calculus, probability theory, statistics, and optimization theory. Must have demonstrated knowledge with developing, training, and evaluating deep neural networks on large amounts of noisy data. Must have demonstrated knowledge with SDLC in monorepos, Google Cloud Platform (GCS, GCE, BigQuery, etc.), and cross-functional collaboration on ML application development. Must have demonstrated knowledge with quantitative finance and at least one AI subfield, such as NLP. Must pass company’s required skills assessment. Employer will accept any amount of graduate coursework, graduate research experience or experience with the required skills.

The base pay for this role will be between $165,000 and $325,000 per year. This role may also be eligible for other forms of compensation and benefits, such as a discretionary bonus, health, dental and other wellness plans and 401(k) contributions. Discretionary bonus can be a significant portion of total compensation. Actual compensation for successful candidates will be carefully determined based on a number of factors, including their skills, qualifications and experience.