Top Tech Jobs & Startup Jobs

Develop options pricing and risk models, improve volatility valuation, research volatility alphas, collaborate with traders, and mentor junior staff.

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.