Top Tech Jobs & Startup Jobs in NYC, NY

Build and optimize large-scale machine learning training and inference systems for real-time trading applications. Responsibilities include distributed training pipelines, experiment automation, GPU and low-level performance optimization, low-latency inference, scalable model infrastructure, framework improvements, and evaluation of ML tooling. The role requires production software engineering, strong ML fundamentals, systems thinking, performance measurement, and collaboration with quantitative researchers and engineering specialists.

Architect and co-design machine learning models for custom hardware, accounting for latency, resource, and numerical constraints. Translate model requirements into hardware architecture decisions, collaborate with hardware engineers to implement and verify inference solutions, and deploy systems from proof of concept through production. Evaluate research in neural architecture search, ML systems, quantization, and compiler infrastructure for measurable performance improvements.

Own end-to-end technical onboarding and certification for electronic trading clients, from connectivity testing through production launch. Coordinate Development, Infrastructure, Compliance, Legal, Clearing, Business Development, and clients; validate FIX connectivity and routing; configure accounts and trading setups; monitor post-launch message quality and behavior; manage readiness, blockers, recertification, regulatory reporting, and post-trade requirements. Serve as the primary responder for onboarding issues and ensure repeatable, operationally disciplined processes.

Conduct quantitative research for proprietary trading strategies by analyzing high-frequency market data, developing statistical and machine-learning models, building simulations and backtesting tools, and evaluating model performance. Collaborate with traders and developers to validate algorithms, improve pricing and execution, assess production readiness, prioritize research initiatives, and integrate validated models into low-latency trading systems. Mentor graduate researchers and interns.

Ten-week machine learning engineering internship focused on building systems for training and deploying large-scale ML models. Interns collaborate with researchers, hardware experts, and software engineers on GPU acceleration, distributed computing, and open-source tools supporting trading strategies. The program includes technical training, mentorship, professional development, financial markets education, and team activities. Successful interns may receive an opportunity for a graduate position.

Conduct ML and deep learning research to build, test, and deploy predictive models for trading. Collaborate with traders, researchers, and engineers to engineer features, preprocess data, prototype algorithms, and improve production research tooling to enhance trading performance.

Serve as a strategic HR advisor to NYC business leads, driving talent strategy, performance coaching, engagement, data-driven insights, change initiatives, employee relations, and cross-HR collaboration to improve retention, development, and organizational effectiveness.

Maintains physical servers, network devices, cabling, racks, and IT infrastructure across colocation data centers. Responsibilities include installations, hardware troubleshooting, vendor RMAs, inventory and shipments, documentation, rack optimization, and coordination with colocation and remote-hands teams. The role also supports office IT infrastructure, requires local travel between facilities, occasional flexible hours, and the ability to lift up to 60 pounds.

Develop, evaluate, and deploy machine learning models for real trading environments; apply rigorous statistical techniques to measure performance, control overfitting, and assess model risk and interpretability; collaborate with traders and developers; maintain best practices and production-ready systems.

10-week in-person research internship partnering with Quant Researchers to explore research ideas, build analysis tools, learn options theory and market making, and develop skills for potential conversion to a full-time Graduate Researcher role.