Senior Research Platform Engineer — HPC & ML

Tradermath; New York (NY); Hybrid; USD 200,000 - 300,000; Full time

Benefits offered by this job

Generous paid time off

Hybrid working opportunities

Free breakfast, lunch, and snacks

Wellness reimbursement

Company-sponsored sports & fitness

Volunteer opportunities

Learning & development opportunities

Job summary

Tower Research Capital is seeking a Senior Platform Engineer to build a multi-tenant, scalable research compute platform across on-premises and cloud environments in NYC. You will collaborate with Quant Researchers, Portfolio Managers, and Infrastructure to deliver elastic, portable compute for large-scale ML workloads.

Responsibilities emphasize elevating researcher experience, implement fault-tolerant pipelines, and optimize data-paths with telemetry and cost-attribution.

Qualifications

• Deep experience with HPC job schedulers (e.g., Slurm) and containerized HPC execution.
• Advanced understanding of Kubernetes architecture, CRDs, HPC-focused operators, and GPU-native schedulers.
• Familiarity with distributed computing frameworks (Ray) and DL frameworks (PyTorch, JAX) with multi-node scaling.

Responsibilities

• Elevate researcher experience by designing platform abstractions, APIs, and a durable job control plane for launching simulations, distributed training, and pipelines.
• Architect multi-tenant scheduling and isolation combining HPC batch scheduling with cloud-native flexibility and tenant isolation across desks.
• Enable multi-datacenter and multi-cloud portability with infrastructure-agnostic execution abstractions.

Skills

HPC administration

Kubernetes (CRDs/ops)

Distributed ML (Ray, PyTorch, JAX)

Workflow orchestration

Multi-tenant architecture

Storage & networking for large-scale

Observability & cost attribution

Leadership mindset

Job description

Tower Research Capital is seeking a Senior Platform Engineer to build a multi-tenant, scalable research compute platform across on-premises and cloud environments in NYC. You will collaborate with Quant Researchers, Portfolio Managers, and Infrastructure to deliver elastic, portable compute for large-scale ML workloads.

Responsibilities emphasize elevating researcher experience, implement fault-tolerant pipelines, and optimize data-paths with telemetry and cost-attribution.