ML Systems Engineer: Scalable Training & Inference

IMC; New York (NY); On-site; USD 175,000 - 250,000; Full time

Job summary

IMC is seeking Machine Learning Engineers to advance large-scale ML training and real-time inference for trading applications. You will collaborate with researchers and engineers to build robust systems, optimize GPU acceleration, and push the envelope on distributed workflows.

You will contribute across ML research engineering and ML performance engineering, ensuring scalable model infrastructure and efficient deployment in production environments.

Qualifications

• 3+ years of ML experience focused on training or inference systems.
• Proficiency in Python and production-grade software engineering.
• Strong ML fundamentals and ability to reason about model performance.
• Systems mindset with data-driven performance analysis.
• Excellent collaboration with researchers and engineers.
• Experience with distributed training and large-scale systems.
• Experience with GPUs and low-level optimization.
• Open-source contributions and cloud exposure are a plus.

Responsibilities

• Develop large-scale distributed training pipelines.
• Automate ML experiments, hyperparameter tuning and model retraining.
• Collaborate with HPC to optimise workflows and reduce compute cost.
• Build low-latency inference pipelines for real-time production predictions.
• Maximise performance using GPU hardware and acceleration libraries.
• Extend ML frameworks and open-source tools to improve performance.
• Design scalable model infrastructure for high-volume trading data.
• Evaluate and roll out third-party tools for ML development.

Skills

ML experience

Python production engineering

ML fundamentals

Systems mindset

Collaboration

Distributed training

GPU optimization

Production inference

Numerics at scale

Cloud exposure

Open-source contributions

Tools

PyTorch

TensorFlow

JAX

CUDA

Triton

Job description

IMC is seeking Machine Learning Engineers to advance large-scale ML training and real-time inference for trading applications. You will collaborate with researchers and engineers to build robust systems, optimize GPU acceleration, and push the envelope on distributed workflows.

You will contribute across ML research engineering and ML performance engineering, ensuring scalable model infrastructure and efficient deployment in production environments.