Research Platform Engineer

New York · USD 200,000-300,000 / year · Hybrid · Posted Oct 2, 2026

At a glance

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.

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 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

Deep experience with HPC job schedulers, 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 and deep learning frameworks 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