Software Engineer - Machine Learning
Qube Research & Technologies - Paris
Posted Mar 3, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Verification
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- Salary
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- 401(k) match
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Schedule
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- Weekend work
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Application
- Cover letter
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- Assessment
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- Deadline
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Where they hire
State eligibility is not yet verified.
About this role
Software Engineer - Machine Learning Paris Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology, and trading expertise has shaped our collaborative mindset, which enables us to solve the most complex challenges. QRT's culture of innovation continuously drives our ambition to deliver high quality returns for our investors. As a Software Engineer - ML you will focus on turning research outputs into robust, scalable, and well-operated production systems. You will directly contribute to our engineering platform and automation efforts that accelerate delivery, improve reliability, and raise the quality bar across the ML and research lifecycle. Your future role within QRT: - Collaborate closely with Research and Machine Learning engineers to transition prototypes into production-grade services, jobs, and pipelines. - Design, implement, maintain and support end-to-end research-to-production workflows (data/feature pipelines, training/inference jobs, batch processes, internal tooling). - Build and improve automation around packaging, configuration, and environment reproducibility. - Develop and maintain CI/CD pipelines to enforce code quality, testing, security checks, and smooth deployments. - Contribute to deployment and runtime operations (containers, orchestration, infrastructure integration), ensuring reliability, performance, and maintainability. - Implement monitoring, alerting, and observability (logs/metrics/traces) for production workloads; define operational runbooks and on-call support procedures when needed. - Troubleshoot and resolve integration and production issues, ensuring optimal performance, robustness, and reliability. - Create and maintain detailed documentation of system
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