Machine Learning Research Engineer

What you'll do

• Run models through training and inference pipelines to validate infrastructure, benchmark performance, and identify bottlenecks.
• Build abstractions and integrate ML tooling with simulation and data frameworks to support rapid prototyping and distributed training.
• Use AI agents and auto-research workflows to generate experiments, stress-test clusters, and share findings with engineering and research teams.

What they ask for

• Deep proficiency in Python and software design principles.
• Hands-on experience with modern ML frameworks such as PyTorch or TensorFlow, and practical understanding of training, evaluating, and deploying models at scale.
• Experience scaling ML workloads across GPUs and multi-node clusters using Ray, Dask, or PyTorch Distributed.
• Familiarity with LLM tooling, agentic frameworks, and AI for automating coding, research, or testing tasks; ability to identify hardware and software bottlenecks.

Nice to have

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

Pay

$200,000-$300,000 USD base salary

Tools

Python, PyTorch, TensorFlow, Ray, Dask, PyTorch Distributed

AI focus

Uses AI agents and auto-research workflows to generate experiments, stress-test distributed clusters, and automate coding, research, or testing tasks.