Lead the design and evolution of large-scale machine learning infrastructure, enabling researchers and engineers to build, train, deploy, and optimize advanced models that directly influence global trading performance.

What You'll Do:

Join a high-performing technology and research environment where machine learning is becoming increasingly central to decision-making and innovation. As a Principal Machine Learning Platform Architect, you will help define the future of machine learning infrastructure, building the systems that support large-scale experimentation, model training, deployment, and operational excellence.

Your Responsibilities Will Include:

1. Designing and developing end-to-end machine learning infrastructure for model training, evaluation, deployment, and monitoring.
2. Driving architectural decisions across data access, compute orchestration, experiment management, model versioning, and deployment pipelines.
3. Collaborating closely with quantitative researchers, engineers, and trading teams to accelerate research and production workflows.
4. Implementing and scaling modern deep learning architectures, including transformers, state-space models, graph neural networks, and temporal convolution networks.
5. Exploring advanced techniques such as self-supervised learning, representation learning, continual learning, and cross-sectional modeling.
6. Building highly scalable, reproducible, and observable machine learning systems within a large-scale data environment.
7. Establishing engineering standards, best practices, and technical frameworks across globally distributed teams.
8. Mentoring engineers and contributing to a culture of technical excellence and innovation.
9. Evaluating emerging developments in machine learning research and infrastructure and translating them into practical business impact.
10. Optimizing distributed training systems, GPU utilization, and production model serving capabilities.

Why Join Us:

1. Shape foundational technology decisions rather than maintaining legacy systems.
2. Contribute to a strategically funded and rapidly expanding machine learning initiative.
3. Build infrastructure that directly supports real-time, high-impact decision-making.
4. Collaborate with world-class researchers, engineers, and technologists across global markets.
5. Work in an environment where innovation, collaboration, and technical rigor are highly valued.
6. Influence the future direction of machine learning capabilities on a global scale.
7. Enjoy close partnerships between research and engineering teams, enabling rapid experimentation and measurable outcomes.

About You:

1. 8+ years of experience designing and building machine learning platforms, infrastructure, or large-scale AI systems.
2. Proven success architecting training and inference platforms from concept through production.
3. Expert-level Python skills with strong experience in CUDA and/or C++.
4. Deep expertise in modern machine learning frameworks such as PyTorch, TensorFlow, or JAX.
5. Strong understanding of deep learning fundamentals, optimization techniques, regularization methods, and scalable model training.
6. Experience with distributed training technologies, GPU optimization, and high-performance computing environments.
7. Demonstrated success deploying production-grade machine learning systems with strong observability and monitoring practices.
8. Comfortable working across the full machine learning lifecycle, from data engineering and training infrastructure to model serving and deployment.

Ready to Build the Future of Machine Learning?

If you're passionate about designing world-class ML platforms, scaling cutting-edge deep learning systems, and working alongside exceptional researchers and engineers to solve complex real-world challenges, we'd love to hear from you. Join a team where your technical vision, architectural expertise, and innovative thinking will help shape the next generation of machine learning-driven decision-making.

#MachineLearning

#MLEngineering

#PrincipalEngineer

#DeepLearning

#AIInfrastructure

#PyTorch

#TensorFlow

#JAX

#DistributedTraining

#GPUComputing

#CUDA

#HighPerformanceComputing

#QuantitativeResearch

#MLOps

#TradingTechnology