Machine Learning Engineer
Tower Research CapitalAbout the Role
Tower Research Capital is seeking a Machine Learning Engineer to architect and develop the next generation of Tower's machine learning research platform. This role focuses on building scalable, reliable infrastructure that enables large-scale experimentation, model training, and simulation across on-premises HPC and multi-cloud environments. You will partner closely with quantitative researchers to understand evolving research workflows and translate them into robust platform capabilities, while designing and optimizing distributed training pipelines for high-throughput, GPU-accelerated workloads.
- Architect and develop scalable ML research platforms with emphasis on reliability and reproducibility
- Build infrastructure for large-scale experimentation and model training across HPC and cloud environments
- Design distributed training pipelines and optimize compute efficiency and resource scheduling
- Enhance platform observability and improve experiment management, model versioning, and data lineage
- Develop tools for feature engineering, dataset generation, and large-scale backtesting
Requirements
- 2+ years of experience designing and building large-scale distributed systems, ideally in support of research or data-intensive workloads
- Strong programming experience in Python, with a focus on writing clean, maintainable, and high-performance code
- Experience developing and operating applications on Linux-based HPC clusters and/or cloud platforms
- Solid understanding of distributed computing concepts, parallel processing, and resource management
- Experience with GPU-based workloads and familiarity with modern ML frameworks (e.g., PyTorch, TensorFlow, JAX)
- Experience optimizing data pipelines and handling large-scale structured and unstructured datasets
- Strong troubleshooting skills with the ability to debug complex, cross-layer system issues
- Ability to work independently in a fast-paced, research-driven environment
- Strong communication skills and experience collaborating directly with researchers or data scientists
- Experience building internal ML platforms or research tooling at scale
- Familiarity with experiment tracking systems, workflow orchestration frameworks, and model lifecycle management
- Experience with containerization and orchestration technologies (e.g., Docker, Kubernetes)
- Exposure to quantitative finance, simulation systems, or other latency- and performance-sensitive domains
What You'll Get
- Generous paid time off policies
- Savings plans and other financial wellness tools available in each region
- Hybrid working opportunities
- Free breakfast, lunch and snacks daily
- In-office wellness experiences and reimbursement for select wellness expenses (e.g., gym, personal training and more)
- Company-sponsored sports teams and fitness events (JPM Corporate Challenge, Cycle for Survival, Wall Street Rides FAR and more)
- Volunteer opportunities and charitable giving
- Social events, happy hours, treats and celebrations throughout the year
- Workshops and continuous learning opportunities