Jobs at Tower Research Capital

Manage end-to-end procurement and strategic sourcing initiatives, including requirements gathering, market analysis, negotiations, contracting, renewals, pricing models, and supplier performance. Partner with senior stakeholders, analyze spend and commercial risks, support logistics and supply continuity, improve procurement governance and systems, and contribute to transformation programs. The role requires strong procurement lifecycle knowledge, negotiation skills, financial analysis, project management, and cross-functional influence.

Develop a multi-tenant research compute platform for large-scale machine learning workloads across hybrid CPU and GPU infrastructure. Responsibilities include HPC and Kubernetes scheduling, workflow orchestration, multi-cloud portability, fault-tolerant pipelines, performance optimization, and observability with cost attribution.

Optimize large-scale machine learning training systems across CPUs, GPUs, and accelerators. Responsibilities include benchmarking, distributed-training optimization, GPU kernel and framework development, end-to-end pipeline efficiency, numerical optimization, and training infrastructure. The role partners with ML researchers, quantitative researchers, HPC engineers, systems engineers, and hardware specialists to improve performance, scalability, utilization, reproducibility, and cost across on-premises and cloud environments.

Build and maintain scalable batch and real-time data pipelines for financial and alternative datasets. Productionize AI/ML workflows for document extraction, classification, and data processing; own model evaluation, monitoring, and human review. Design investment data models, validation, anomaly detection, and reconciliation systems. Partner with quants and data scientists to operationalize research signals, onboard vendors and datasets, integrate APIs, and improve data reliability using modern cloud, streaming, orchestration, and warehouse technologies.

Analyze and improve structured and unstructured financial data pipelines using Python, SQL, statistical methods, machine learning, and LLMs. Responsibilities include data cleansing, validation, anomaly detection, reconciliation, dataset onboarding, API integration, model evaluation, workflow monitoring, documentation, and root-cause investigation. The role also involves explaining technical findings, managing assigned datasets, and supporting reliable production data processes in a quantitative trading environment.

Support, maintain, and enhance Linux trading and HPC research infrastructure. Responsibilities include emergency response, planned changes, deployment projects, HPC and Condor management, performance troubleshooting, automation, monitoring, core network services, and on-call support. The role collaborates directly with trading and engineering teams and stays current with HPC, storage, and GPU technologies.

Operates and supervises automated trading strategies alongside quantitative traders and developers. Monitors production trading for risk, compliance, and unusual market activity; troubleshoots day-to-day issues; analyzes trades and market data; develops Python scripts and applications to improve trading infrastructure and strategies. Requires early morning coverage aligned with market hours and a strong understanding of futures markets, algorithmic trading systems, and Linux environments.

Develop and improve Tower’s internal agentic harness for autonomous coding, model building, long-horizon tasks, memory management, and multi-agent orchestration. Integrate tools and MCP systems, support current LLMs, identify automation opportunities with quantitative teams, and ensure regulatory compliance and production isolation. The role requires software engineering, data science, or ML engineering experience and strong knowledge of agentic frameworks and distributed systems.

Design AI systems that automate fundamental equity research, analyze financial data to generate earnings predictions and price targets, and integrate fundamental insights into systematic trading strategies. The role researches industry sectors, optimizes AI workflows, and supports iterative testing of fundamentally driven strategies. Candidates need finance or related education, at least two years of fundamental equity analyst experience, strong financial modeling skills, and the ability to translate investment insights into systematic strategy inputs.

Build and maintain internal tools, data workflows, and web applications. Develop Python services, ETL and reporting pipelines, SQL queries, and AI-enabled internal tools (chatbot/agent) integrating with APIs and databases. Maintain backend APIs and frontends, improve reliability, observability, testing, and CI/CD, and collaborate with business users to deliver practical solutions.

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.