Research Engineer - Systematic Futures

NJF Global Holdings Ltd; New York (NY); On-site; USD 180,000 - 250,000; Full time

Job summary

NJF Global Holdings Ltd in New York seeks a senior engineer blending quant and development skills to shape the research stack. You’ll implement backtesting, tick-data pipelines and fast testing, accelerating model ideas toward live trading.

Expect to co-design features, optimize performance, and collaborate with researchers and engineers to scale workloads across cores or clusters, with futures tick data expertise highly valued.

Qualifications

• Senior engineer with 4+ years buy-side experience and emphasis on fast Python.
• Strong knowledge of NumPy and Pandas for tick data processing.
• Experience with vectorisation, Numba, and optional C++ integration.
• Ability to test models and challenge researchers’ approaches.

Responsibilities

• Build and maintain backtesting environment for tick data.
• Design data pipelines for clean, aligned futures tick data.
• Enable fast hypothesis sweeps and multi-year tests.
• Develop shared libraries for features, testing and plotting.

Skills

Python

NumPy

Pandas

Numba

Parallel computing

Dask

Ray

Performance profiling

Backtesting

Futures tick data

Data pipelines

Parquet

Kdb+

Statistics

OLS

Hypothesis testing

Sharpe ratio

Regression

PCA

Gradient boosting

C++ integration

pybind11

Cython

CPython C API

Tools

pybind11

Cython

CPython C API

Job description

The group is a small, purely systematic futures trading team in New York, led by a portfolio manager who joined within the last year and now runs a team of around eight. It trades intraday and extraday strategies, with holding periods from minutes up to a few days, across all major futures markets, with a focus on commodity, interest rate, FX and equity index futures. Its strategies include cross-sectional alpha, statistical arbitrage, relative value and short-term directional trading, all built on tick data. It runs both asset-class-specific and generic alphas. It is not a CTA and does not run traditional trend following. The team works collaboratively, with researchers and engineers sharing ideas and code.

This role sits between research and engineering. Researchers own the signals and strategy logic. This role builds the tools and infrastructure that let them test ideas quickly and move good ones towards live trading. The team is still being built, so the research stack is being shaped now, and the work in this seat sets how fast the whole group can innovate.

You will build and maintain the backtesting environment the researchers use every day, from tick data handling and event-driven simulation to fast vectorised testing. You will design data pipelines that clean, align and store futures tick data, including contract rolls and continuous series. You will make research run faster through parallel and distributed computing, so that large parameter sweeps and multi-year tests finish in minutes rather than hours. You will also build the shared libraries for features, signal testing, performance analysis and plotting, and work closely with researchers on the models themselves.

Target profile

We are looking for a senior engineer with 4+ years of buy-side experience who is part quant, part quant developer. You write fast, clean Python and know NumPy and Pandas deeply, including when to vectorise, when to drop down to Numba or C++, and how to measure the result rather than guess. You have enough statistics to follow and challenge how researchers build and test models, and a real interest in how futures markets behave at the tick level. You will stand out if you have built a backtester or research platform at a trading firm, scaled research workloads across many cores or a cluster, worked with futures tick data and its quirks, or written C++ and integrated it with Python (pybind11, Cython or the CPython C API).

Skills and expertise

• Python (NumPy, Pandas, Numba)
• Parallel and distributed computing (multiprocessing, Dask, Ray)
• Performance profiling and optimisation
• Backtesting and simulation, event-driven and vectorised
• Futures tick data handling, contract rolls and continuous series
• Data pipelines and storage for large time series (Parquet, kdb+ or similar)
• Statistics and regression (OLS, hypothesis testing, Sharpe ratio evaluation)
• Statistical learning (regularisation, PCA, gradient boosting)
• Linux and cluster computing
• C++ and C/Python integration (pybind11, Cython)