Quantitative ML Engineer

# Quantitative ML Engineer — Tick Data, Feature Engineering and Predictive Modeling We have an existing systematic trading strategy and execution system. We are seeking a hands-on quantitative ML engineer to build and manage the dataset and modeling pipeline that supports the strategy. The selected candidate will take historical tick data and candidate trade events, construct an event-level ML dataset, engineer predictive features, and train models that score which opportunities are most likely to produce favorable continuation or returns. Some existing data-processing components are written in Rust. Most ML research may be conducted in Python, but you must be comfortable working with or integrating into a Rust-based data pipeline. Direct Rust development experience is strongly preferred. ## Responsibilities - Process and manage historical tick, trade, and quote data - Build reproducible event-level modeling datasets - Maintain and improve existing Python and Rust data pipelines - Engineer market, microstructure, liquidity, regime, and security-relative features - Research and integrate alternative data where it may add predictive value - Define labels and prediction targets around the existing strategy - Train and compare statistical and machine-learning models - Perform walk-forward and out-of-sample validation - Prevent look-ahead bias, target leakage, survivorship bias, and overfitting - Monitor datasets, features, and model performance over time - Deliver a scoring model that can be integrated into the existing system - Clearly document the dataset, experiments, results, and limitations ## Required Experience - Strong Python and machine-learning experience - Financial time-series, tick-data, or high-frequency data experience - Feature engineering for sequential or event-based data - Time-aware model validation - Building and maintaining reproducible data pipelines - Ability to work independently and take ownership of the data and ML process Experience with Rust, market microstructure, systematic trading, alternative data, Parquet, Polars, DuckDB, SQL, or cloud-based datasets is highly desirable. ## Project Structure The trading strategy and execution platform already exist. You will not be responsible for inventing the strategy or building the brokerage execution system. The work will be siloed. You will receive the relevant data, candidate-event records, prediction objectives, and enough information to understand the strategy’s decision context. Access to brokerage accounts, portfolio logic, and the complete production system is not required. We will begin with a paid pilot using a limited historical period and security universe. A successful pilot may lead to an ongoing engagement managing the datasets, engineering additional features, researching alternative data, retraining models, and monitoring performance. Individual freelancers are preferred over agencies. ## Application Questions Please begin your proposal with “TICK DATA” and answer: 1. What experience do you have building ML datasets from tick or other high-frequency financial data? 2. How did you prevent leakage and validate the model over time? 3. What experience do you have with Rust-based data pipelines? 4. Have you evaluated or integrated alternative data into financial models? 5. How many hours per week can you consistently dedicate?