A global quantitative trading firm is hiring a Machine Learning Research Engineer to build the systems that enable researchers to train, evaluate, and refine large-scale models.
Working closely with quantitative researchers and infrastructure specialists, you’ll make experimentation faster, distributed training more efficient, and the transition from research to production more reliable. This is an engineering role with direct exposure to the research process and its impact on trading.
What you’ll do
- Develop distributed training pipelines that support complex models and large datasets.
- Build tools that automate experimentation, hyperparameter searches, and model retraining.
- Improve data pipelines, parallelism strategies, and checkpointing to support training at scale.
- Partner with HPC specialists to increase training throughput and reduce compute costs.
- Extend ML frameworks and open-source components to better support research workloads.
- Design scalable infrastructure that helps researchers work effectively with high-volume trading data.
- Evaluate and integrate technologies that improve model development, evaluation, and deployment.
- Own systems through testing, rollout, and ongoing operation, incorporating feedback from researchers.
What you’ll bring
- 3+ years of relevant ML experience, with a focus on training systems or ML research infrastructure.
- Experience with frameworks such as PyTorch, TensorFlow, or JAX, with an understanding that extends beyond their high-level interfaces.
- Expertise in distributed training or large-scale ML systems, including parallelism, data processing, and checkpointing.
- Strong Python engineering skills, rigorous testing practices, and responsibility for systems after deployment.
- The ability to reason about model behavior, numerical correctness, and system performance.
- An evidence-led approach to scalability and reliability, including thoughtful build-versus-buy decisions.
- Strong collaboration skills and the ability to translate researchers’ needs into practical engineering solutions.
Experience with GPU optimization, numerical behavior at scale, cloud platforms, or orchestration tools is beneficial. Contributions to open-source ML, data science, or distributed systems projects are also valued.