Top Tech Jobs & Startup Jobs

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.

Provide Level 1 and Level 2 support for in-house trading applications, resolve complex production incidents, and partner with trading and engineering teams during market hours. Automate repetitive support workflows, modernize monitoring and alerting, oversee deployments and configuration changes, and participate in on-call support. The role requires Python and Bash scripting, Linux, SQL, Git, distributed-systems troubleshooting, and production engineering experience.

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.

Monitor automated trading systems across Asian market hours, analyze live activity for risk and compliance issues, respond to unusual behavior and market changes, and coordinate real-time issue resolution with risk managers, traders, engineers, brokers, and exchanges. Maintain monitoring procedures and production issue documentation while supporting trading across equities, futures, options, and FX markets.

Develop low-latency electronic trading infrastructure, including network and systems programming, data collection and visualization systems, market-data pipelines, algorithmic trading systems, and AI-assisted automation products. The role requires strong programming and object-oriented design knowledge, familiarity with C++, Golang, Java, Python, or Rust, SQL, relational databases, and Linux, plus fluent written and spoken French and English.

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.

Design, build, and maintain large-scale data infrastructure, pipelines, and systems supporting data analysis and electronic trading strategies. Develop storage, compute, and networking resources handling petabytes of data and tens of thousands of requests. Collaborate with data scientists, portfolio managers, traders, and IT teams to deliver reliable, secure, and scalable solutions.

Participate in a rotational engineering program building high-performance, low-latency electronic trading infrastructure. Develop market data, execution, research, simulation, risk management, monitoring, and performance systems using C++. Collaborate with quantitative researchers, traders, and engineers to deploy systematic trading strategies, optimize production systems, and diagnose performance or data issues. Rotate across engineering teams before transitioning to a permanent placement.

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.