Machine Learning Engineer (Quantitative Finance)

Location: New York, United States

Salary: $200,000–$300,000 per year, excluding bonuses, benefits, and other compensation.

Company

hirify.global operates in quantitative finance and systematic trading, developing technology and infrastructure for quantitative research.

What you will do

• Design and build experiment tracking, job orchestration, and reproducibility infrastructure for machine learning research.
• Develop tools for simulation workflows, including historical back-tests, production monitoring, and simulator enhancements.
• Monitor GPU cluster allocation and utilization, identify bottlenecks, and improve compute efficiency.
• Diagnose performance issues across training pipelines, including data loading, storage I/O, GPU utilization, and inter-node communication.
• Build versioned financial-data pipelines and feature storage systems for fast, reproducible access to training data.
• Work with researchers and infrastructure engineers on workflow improvements, capacity planning, and cloud or on-premise tooling decisions.

Requirements

• 5+ years of experience in machine learning engineering, research infrastructure, or HPC environments.
• Strong Python engineering skills; exposure to C++ in performance-sensitive contexts is beneficial.
• Experience building or operating distributed training infrastructure and understanding collective communication libraries such as NCCL or Horovod.
• Practical experience with experiment tracking systems and research infrastructure.
• Working knowledge of Linux systems, storage, networking, and job scheduling.
• Excellent communication skills and the ability to collaborate with researchers and engineers across disciplines.

Nice to have

• Experience with on-premise compute environments and Slurm or similar job orchestration tools.
• Familiarity with GPU profiling and optimization tools such as NSight Systems and PyTorch Profiler.
• Experience with Parquet, Arrow, Polars, Prefect, Dagster, or similar tools.
• Experience with high-stakes time-series data at scale, quantitative finance, or algorithmic trading.
• Experience contributing to open-source machine learning frameworks or infrastructure tooling.

Culture & Benefits

• Collaborative work across quantitative researchers, infrastructure engineers, and technologists.
• Opportunity to shape machine learning infrastructure and research workflows as capabilities scale.
• Compensation includes salary plus bonuses, benefits, and other categories of compensation.
• Inclusive workplace and equal employment opportunity commitment.