We are seeking a Machine Learning Framework Developer to spearhead the development and enhancement of our quantitative trading model frameworks. This role is instrumental in optimizing our proprietary trading algorithms and streamlining our trading processes.
What you'll do:
- Responsible for the overall architecture and design of the machine learning compute platform, ensuring optimal performance, scalability, and reliability.
- Collaborate with ML researchers, data scientists, and other engineers to understand their computational requirements and translate them into platform features.
- Lead the evaluation, integration, and deployment of new technologies and tools to enhance the capabilities of the platform.
- Ensure that the platform is designed with best practices in mind, allowing for efficient training, inference, and deployment of machine learning models.
- Monitor the latest advancements in machine learning hardware, including GPUs, TPUs, and custom ASICs, and make necessary architectural adjustments to leverage these technologies.
What you need:
- Master's or Ph.D. degree in Computer Science, Electrical Engineering, or a related field; with significant work experience in relevant domains.
- Deep understanding of distributed systems, high-performance computing, and scalable machine learning architectures.
- Familiarity with various ML frameworks like TensorFlow, PyTorch, and MxNet, and an understanding of their internal workings and optimization techniques.
- Hands-on experience with cloud platforms, containerization technologies (e.g., Docker, Kubernetes), and orchestration tools.
- Strong analytical and problem-solving skills, combined with a keen attention to detail and a high level of initiative.
Good to have:
- Previous experience in building or scaling machine learning platforms in large-scale environments.
- Knowledge of network protocols, storage solutions, and security best practices related to machine learning deployments.
- Active participation in the machine learning and distributed systems community through publications, talks, or open-source contributions.