Top Tech Jobs & Startup Jobs in NYC, NY

Conduct fundamental credit analysis of convertible issuers, evaluating capital structures, liquidity, covenants, recovery, and refinancing risk. Assess stressed credit positions and support portfolio sizing, hedging, and exits. Analyze primary market transactions, monitor portfolio exposures, develop issuer views, and identify emerging risks. Build scalable, data-driven credit analysis tools using AI, LLMs, and agentic workflows while presenting concise recommendations to portfolio managers.

Lead research and production development for LLM post-training in quantitative finance. Responsibilities include designing RLHF, DPO, reward modeling, and policy optimization methods; building preference-data and training infrastructure; managing large-scale compute; developing evaluation frameworks; collaborating with quantitative researchers; and supporting production systems.

Conduct AI and machine learning research during a 10-week summer internship. Develop techniques and infrastructure, write code, run experiments, analyze noisy financial data, and collaborate with experienced researchers. The role focuses on deep learning, large language models, reinforcement learning, data pipelines, cloud computing, and multi-machine research environments.

Research and develop execution models, trading tactics, market impact models, simulators, scheduling methods, and short-horizon signals. Analyze order book, quote, trade, order, and fill data to improve execution costs and capacity. Design production experiments, evaluate execution metrics, and deploy research into trading systems while collaborating with portfolio management, modeling, engineering, and trading teams.

Manage investor relations and client services for current and prospective investors. Responsibilities include handling fund inquiries, producing investor materials, overseeing subscriptions, redemptions and transfers, maintaining CRM and client portal capabilities, improving data integrity, and leading technology-driven service initiatives. The role collaborates with internal teams and fund administrators, establishes operational governance and quality standards, and may lead or mentor associates.

Conduct quantitative research for macro and fixed-income markets by developing alpha-generating investment models. Responsibilities include idea generation, feature development, alpha forecasting, monetization, portfolio construction, portfolio management, dataset analysis, and applying artificial intelligence and machine learning techniques. The role involves collaborating with quantitative researchers and developers to solve complex investment problems and communicate findings clearly.

Monitor systematic trading strategies, identify portfolio and investor risks, and escalate issues to trading, portfolio management, engineering, and modeling teams. Develop automated alerts, reporting, and quantitative analysis tools; investigate root causes; analyze strategy behavior and risk; document and improve operational risk processes; and collaborate on initiatives across diverse partner teams.

Conduct AI and machine learning research for financial-market applications, developing predictive models, infrastructure, prototypes, and production systems. Apply deep learning, large language models, and reinforcement learning to noisy data. Write code, run experiments on cloud and CPU/GPU infrastructure, collaborate across teams, contribute to products, and engage with internal and external research communities through publications and conferences.

Develop and optimize low-latency quantitative software for research, feature construction, alpha generation, and trading. Partner with quantitative researchers to prototype and implement components in Rust, C++, and Python, analyze real-time and simulated trading data, and research improvements to models, feature pipelines, and strategies. Build deep expertise in research systems, quantitative domains, and financial markets while delivering high-quality, high-performance software.

Designs and optimizes CUDA kernels and GPU workloads for financial computation, improving throughput, latency, memory efficiency, and numerical precision across modern NVIDIA hardware. Develops reusable GPU libraries, profiles workloads with NVIDIA tools, manages mixed-precision computation, and evaluates frameworks such as RAPIDS, CUTLASS, cuBLAS, and TensorRT. Collaborates with quantitative modeling teams and may support multi-GPU and multi-node programming.