Remote-Eligible Quant Developer for Research Systems

Anemoi Predictive Technology LLC.; Sacramento (CA); Remote; USD 185,000 - 260,000; Full time

Benefits offered by this job

Medical, dental, and vision plan

401(k) plan with company matching

Flexible paid time off

Parental and caregiver leave

Learning and development budget

Remote-work equipment

Performance bonus and equity eligible

Job summary

Anemoi Predictive Technology LLC. in Sacramento, CA is seeking a Quantitative Developer — Research Systems to build the research platform used for reproducible market studies, with Pacific Time monitoring of policy-sensitive events, the U.S. close, and after-hours review.

Responsibilities include owning research-platform reliability, experiment lineage, and large time-series workloads; developing reusable data and backtest infrastructure; and improving test coverage and data integrity.

Qualifications

• Production Python and SQL experience.
• Strong knowledge of distributed data systems and numerical workloads.
• Experience supporting quantitative researchers.

Responsibilities

• Develop reusable data and backtest infrastructure.
• Improve experiment tracking, lineage, and reproducibility.
• Optimize large time-series workloads.
• Add tests that detect leakage and inconsistent data vintages.
• Own research-platform reliability, experiment lineage, and large time-series workloads during Pacific Time monitoring of policy-sensitive events, the U.S. close, and after-hours review.

Skills

Python

SQL

Distributed data systems

Quantitative researcher support

Job description

Anemoi Predictive Technology LLC. in Sacramento, CA is seeking a Quantitative Developer — Research Systems to build the research platform used for reproducible market studies, with Pacific Time monitoring of policy-sensitive events, the U.S. close, and after-hours review.

Responsibilities include owning research-platform reliability, experiment lineage, and large time-series workloads; developing reusable data and backtest infrastructure; and improving test coverage and data integrity.