Top Tech Jobs & Startup Jobs in NYC, NY

Develop and improve the research framework and tools for a quantitative trading team. Provide day-to-day on-site support to quantitative researchers, implement production-grade Python and C++ code, and integrate statistical libraries and workflow/task scheduling solutions. Collaborate with traders and engineers to enhance research infrastructure and execution capabilities.

Design, implement, and optimize high-performance, low-latency trading infrastructure for research and production. Partner with researchers and traders to build market data, execution, backtesting, and deployment systems. Develop tools for prototyping, simulation, monitoring, risk management, and performance analysis across the strategy lifecycle.

As a Senior Quantitative Researcher, you will analyze large data sets, build statistical models, implement trading strategies, and develop quantitative research tools.

The Senior Systems Engineer will architect and implement scalable cloud infrastructure, manage Kubernetes clusters, automate workflows, and collaborate with teams to optimize performance-critical workloads.

Provide first-line operational support for the Research HPC compute fleet: monitor fleet health, troubleshoot job/scheduler/storage issues, provision and maintain Linux machines, follow runbooks, triage incidents, document runbooks and KB articles, and escalate or drive fixes to resolution.

Act as 'Customer Zero' for the internal ML research platform: benchmark and stress-test distributed training and inference stacks, enable rapid prototyping and agentic/auto-research workflows, identify system bottlenecks across hardware and software, document empirical findings, and advise engineering and research teams on platform improvements.

Design and build a zero-touch machine lifecycle pipeline: automate out-of-band firmware/BIOS management, network boot OS provisioning, Ansible-based configuration, CI/CD-driven GitOps reconciliation, automated validation/burn-in, lifecycle state machine, and instrumentation for fleet-scale datacenter operations.

Design, deploy, and operate large distributed GPU clusters. Profile and optimize GPU workloads across compute, storage, and network. Build automation for provisioning, monitoring, diagnostics, and self-healing. Qualify new hardware, partner with researchers and vendors, and own infrastructure projects end-to-end.

Design and optimize ultra-low-latency ML inference pipelines across CPUs, GPUs, and FPGAs. Benchmark heterogeneous platforms, develop high-performance GPU kernels, resolve memory and interconnect bottlenecks, and apply model reduction (quantization, pruning, distillation) to meet microsecond-level latency for production trading systems while collaborating with ML researchers, HPC, FPGA, and datacenter teams.

Build, operate, and improve software delivery and developer infrastructure for large-scale C++ and Python applications. Design and run CI/CD pipelines, build systems, artifact management, infrastructure automation, observability, containerized workflows, and developer self-service tooling. Partner with engineers, SRE, and global platform teams to improve performance, reliability, security, and developer productivity.