8 months ago
London, United KingdomSenior
Responsibilities
- Develop greenfield solutions for complex machine-learning workflow and interdependency requirements.
- Implement feature and model stores and establish best practices for versioning features, data, and models.
- Improve inference compute utilization through model serving.
- Build reliable ML CI/CD workflows and support complex job dependency graphs.
- Improve production robustness through validation and monitoring.
- Own solutions end to end from problem articulation through delivery and collaborate with technical and non-technical stakeholders.
Requirements
- Industry experience applying software engineering and MLOps best practices.
- Strong software engineering ability and an appreciation of good architecture.
- Ability to collaborate with and influence technical and non-technical people.
- Proven ability to take end-to-end ownership of solutions.
- Effective decision-making with a focus on the medium and long term.
- Ability to prioritize independently and pragmatically.
- Finance experience is not required; candidates from non-financial backgrounds are encouraged to apply.
Benefits
- Highly competitive compensation plus an annual discretionary bonus.
- Lunch provided via Just Eat for Business and a dedicated barista bar.
- 35 days of annual leave.
- 9% company pension contributions.
- Informal dress code and work/life balance support.
- Comprehensive healthcare and life assurance.
- Cycle-to-work scheme.
- Monthly company events.
- London HQ work environment.
Categories
About G-Re
G-Research is a London-based quantitative research and technology firm that develops models, data pipelines, and trading systems to forecast movements in global financial markets. It employs researchers and software engineers to build machine-learning-driven strategies and the infrastructure that supports systematic investing, spanning high-performance compute and secure networks. Privately held and founded in 2001, the company operates from its London headquarters and focuses on institutional, data-intensive finance.