IMC is looking to build systems that drive the training and deployment of large-scale ML models across global operations to accelerate experimentation cycles and foster continuous innovation and refinement in trading strategies.
Requirements
- Hands-on experience with real-time, low-latency ML pipelines in high-performance environments
- Strong engineering skills, including Python, CUDA, or C++
- Knowledge of machine learning frameworks such as PyTorch, TensorFlow, or JAX
- Proficiency in GPU programming for training and inference acceleration (e.g., CuDNN, TensorRT)
- Experience with distributed training for scaling ML workloads (e.g., Horovod, NCCL)
- Exposure to cloud platforms and orchestration tools
- A track record of contributing to open-source projects in machine learning, data science, or distributed systems
Responsibilities
- Develop large-scale distributed training pipelines to manage datasets and complex models
- Build and optimize low-latency inference pipelines, ensuring models deliver real-time predictions in production systems
- Develop libraries to improve the performance of machine learning frameworks
- Maximize performance in training and inference using GPU hardware and acceleration libraries
- Design scalable model frameworks capable of handling high-volume trading data and delivering real-time, high-accuracy predictions
- Collaborate with quantitative researchers to automate ML experiments, hyperparameter tuning, and model retraining
- Partner with HPC specialists to optimize workflows, improve training speed, and reduce costs
Other
- 5+ years of experience in machine learning with a focus on training or inference systems
- Base Salary range: $175,000—$250,000 USD
- Eligible for a discretionary bonus and benefits, including paid leave and insurance
