Machine Learning Research Engineer (AI/ML)

Location: New York, United States; hybrid working opportunities available

Annual base salary: $200,000–$300,000, plus an eligible discretionary bonus.

Company

Quantitative trading firm building electronic trading infrastructure, machine learning systems, and research technology for independent trading teams within a global organization.

What you will do

• Validate the central machine learning infrastructure by running complex models through full training and inference pipelines.
• Benchmark distributed ML software and hardware, identify bottlenecks, and stress-test the platform before broader rollout.
• Build abstractions and unified tooling that connect machine learning workflows with simulation and data frameworks.
• Enable rapid iteration on real-world data and distributed training using Ray.
• Develop AI-agent and auto-research workflows for experiment generation, cluster testing, and infrastructure analysis.
• Document system and hardware performance findings and advise engineering and research teams on platform improvements.

Requirements

• Deep proficiency in Python and software design principles.
• Experience building clean, scalable APIs and abstractions.
• Hands-on experience with modern machine learning frameworks such as PyTorch or TensorFlow.
• Practical experience training, evaluating, and deploying models at scale across GPUs and multi-node clusters.
• Experience with distributed computing frameworks such as Ray, Dask, or PyTorch Distributed.
• Ability to profile systems and debug hardware and software bottlenecks, including memory limits, GPU utilization, and data pipeline latency.

Nice to have

• Experience in quantitative finance or complex algorithmic research.
• Familiarity with large-scale time-series data, simulation engines, or performance benchmarking.
• Experience bridging systems engineering and applied machine learning research.

Culture & Benefits

• Collaborative, results-oriented workplace with an open-concept office and casual dress code.
• Generous paid time off and regional savings and financial wellness plans.
• Daily breakfast, lunch, and snacks.
• In-office wellness experiences, selected wellness expense reimbursement, sports teams, and fitness events.
• Volunteer opportunities, charitable giving, social events, and continuous learning workshops.