ML Research Platform Engineer

Tower Research Capital; New York (NY); Hybrid; USD 200,000 - 300,000; Full time

Benefits offered by this job

Hybrid working opportunities

Generous PTO

Free meals in office

Job summary

Tower Research Capital, a leading quantitative trading firm in NYC, is seeking an AI/ML Applied Research Engineer to join our engineering teams. You will sit at the cutting-edge intersection of our ML infrastructure and research groups, serving as Customer Zero for our internal ML platform.

You will expand the platform with rapid prototyping, benchmarking, and stress-testing across software and hardware, leverage AI agents and auto-research workflows, and share insights to guide engineering

Qualifications

• Strong software engineering foundation with Python proficiency and API design.
• Applied machine learning using modern frameworks (PyTorch, TensorFlow) with model deployment experience.
• Experience scaling ML workloads across GPUs and multi-node clusters using Ray, Dask, or PyTorch Distributed.

Responsibilities

• Platform Validation & Infrastructure Benchmarking: test full ML pipeline end-to-end and identify bottlenecks.
• Streamline Rapid Prototyping for ML Research: build abstractions to reduce setup friction and enable rapid iteration.
• Agentic Workflows for ML Research: leverage AI agents to automate experiments and stress-test distributed clusters.
• Research Platform Feedback & Insights Sharing: document findings and advise researchers on stack improvements.

Skills

Python programming

Software design

APIs

AI/ML intuition

Distributed compute mindset

Tools

PyTorch

TensorFlow

Ray

Dask

PyTorch Distributed

Job description

Tower Research Capital, a leading quantitative trading firm in NYC, is seeking an AI/ML Applied Research Engineer to join our engineering teams. You will sit at the cutting-edge intersection of our ML infrastructure and research groups, serving as Customer Zero for our internal ML platform.

You will expand the platform with rapid prototyping, benchmarking, and stress-testing across software and hardware, leverage AI agents and auto-research workflows, and share insights to guide engineering