Research Platform Engineer

Tower Research Capital • New York, NY

7d • $200,000 - $300,000 • Hybrid

About The Position

As part of Tower Research's Core Engineering team, you will develop a multi-tenant research compute platform capable of dynamically orchestrating large scale ML workloads across a hybrid compute infrastructure of GPUs and CPUs. Your primary mission is to work closely with Quant Researchers, Portfolio Managers, and Infrastructure teams, to build a unified, elastic, and multi-tenant research substrate.

Requirements

• Deep experience with HPC job schedulers (e.g., Slurm), including gang scheduling, fair-share priority trees, topology-aware node allocation, and containerized HPC execution
• Advanced understanding of Kubernetes architecture, CRDs, HPC focused operators, admission controllers and GPU-native schedulers
• Familiarity with distributed computing frameworks (e.g., Ray) and deep learning frameworks (e.g., PyTorch, JAX) with multi-node scaling primitives
• Hands-on experience evaluating, architecting, and operating job graph orchestration frameworks
• Experience designing vendor-agnostic infrastructure layers, cloud-bursting strategies, and compute execution spanning multiple on-premise datacenters and public cloud environments
• Familiarity with high-performance storage solutions and high-speed network fabrics for large scale research workloads
• Proven track record building cluster-wide telemetry and cost-attribution platforms for multi-tenant environments
• A strong platform engineering mindset focused on reducing friction for researchers while maintaining rigorous operational efficiency, cost transparency, and system scalability

Responsibilities

• Design intuitive platform abstractions, APIs, scheduler wrappers, and a durable job control plane so researchers can seamlessly launch simulations, distributed training runs, and complex research pipelines without incurring infrastructure overhead
• Build a multi-tenant compute substrate combining HPC-grade batch scheduling (gang scheduling, topology awareness, fair share) with cloud-native flexibility, enforcing strict tenant isolation across trading desks
• Establish infrastructure agnostic execution abstractions that enable compute workloads to run transparently across multiple on-premise datacenters or burst into external cloud providers
• Evaluate, select, and integrate production-grade job graph orchestration engines to automate multi-stage feature, training, and backtesting pipelines
• Implement automated failure detection, retry-on-fault mechanisms, and high-performance checkpointing to ensure long-running distributed jobs survive hardware degradation and faults without manual intervention
• Profile and eliminate I/O bottlenecks, ensuring distributed ML workloads align with high-speed network fabrics and high-performance storage
• Implement comprehensive telemetry to track compute utilization, queue pressure, and GPU/CPU cost attribution, giving Portfolio Managers and Senior Management clear visibility into resource consumption and ROI

Benefits

• 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

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed