T

Tower Research Capital

Trades financial markets with computer models

Research Platform Engineer

Platform EngineeringConfirmed live in the last 24 hours
Full-timeNew York, NY, USA

About the Job

Requirements

  • Deep experience with high-performance computing job schedulers such as Slurm, including gang scheduling, fair-share priority trees, topology-aware node allocation, and containerized high-performance computing execution.
  • Advanced understanding of Kubernetes architecture, custom resource definitions, high-performance computing-focused operators, admission controllers, and GPU-native schedulers.
  • Familiarity with distributed computing frameworks such as Ray and deep learning frameworks such as PyTorch and JAX, including 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 across multiple on-premise datacenters and public cloud environments.
  • Familiarity with high-performance storage solutions and high-speed network fabrics for large-scale research workloads.
  • A proven track record building cluster-wide telemetry and cost-attribution platforms for multi-tenant environments.
  • A platform engineering mindset focused on reducing friction for researchers while maintaining operational efficiency, cost transparency, and system scalability.

Responsibilities

  • Design platform abstractions, application programming interfaces, scheduler wrappers, and a durable job control plane so researchers can launch simulations, distributed training runs, and research pipelines without infrastructure overhead.
  • Build a multi-tenant compute substrate combining high-performance computing batch scheduling with cloud-native flexibility, and enforce strict tenant isolation across trading desks.
  • Establish infrastructure-agnostic execution abstractions so compute workloads can run 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 mechanisms, and high-performance checkpointing so long-running distributed jobs can withstand hardware degradation and faults without manual intervention.
  • Profile and eliminate I/O bottlenecks, aligning distributed machine-learning workloads with high-speed network fabrics and high-performance storage.
  • Implement telemetry to track compute utilization, queue pressure, and GPU/CPU cost attribution, providing Portfolio Managers and senior management visibility into resource consumption and return on investment.

About the Company

Tower Research Capital

Trades financial markets with computer models

Tower Research Capital trades financial instruments with computer models and supplies liquidity, or ready buying and selling prices, to other market participants. Its researchers test strategies against historical data before deploying them through a platform built largely in-house. The platform combines market data, order routing and risk controls, letting traders act on signals across stocks, futures, options and currencies. It earns trading profits by taking positions for its own account, and its liquidity businesses trade directly with counterparties rather than managing their investments. Tower was founded in 1998 and is headquartered in New York City.

Company size
1,001-5,000
Company stage
Not listed
Total funding
$116.1M
Headquarters
New York City, New York
Founded
1998

Recently Posted Jobs

Explore more opportunities at Tower Research Capital

See more jobs