Staff Software Engineer, Strategy Platform

Python Hybrid

Voleon is a quantitative hedge fund that uses machine learning as its core investment approach across a wide range of regions and asset classes. Voleon is a multibillion-dollar asset manager where the size of the engineering organization still allows for direct influence across key systems.

Strategy Platform owns the infrastructure between quantitative research and live trading. Our systems orchestrate the transformation of external market data into features consumed by ML-driven trading strategies, manage strategy deployments from research handoff through production, and provide the tooling that lets researchers iterate quickly without compromising production reliability.

This is a central platform team. We are consolidating and unifying systems that grew organically to serve different parts of the trading lifecycle, building and migrating to shared abstractions across data ingestion, feature computation, deployment and production trading operations. The team balances deep knowledge of how our trading strategies operate with building foundational layers that support the company’s continued rapid growth.

The Role

You will own significant pieces of our platform end to end: designing, building, operating, and evolving them. Concretely:

Own how data is accessed, validated, orchestrated, and catalogued across research and production
Engineer smooth deployment processes for research experiments into production
Develop tooling to integrate data from diverse vendors, unifying symbol mappings for data consistency
Support data pipelines with strong temporal semantics under a range of latency and correctness requirements
Sequence platform migrations that move the firm toward shared abstractions while minimizing disruption to active trading systems
Lead complex projects spanning the company, collaborating across research, legal, trading, finance operations, data, and infrastructure teams
Build tooling to support integration with new assets and markets
Improve observability across the strategy lifecycle, including data cataloguing, experiment tracking, and production SLAs

You might be a good fit if you have:

5+ years of experience in backend, data pipelines, or platform engineering

Owned platform systems that other teams depend on daily, made real decomposition decisions (data access layers, API versioning, data models, migration sequencing), and improved those systems while they were actively in use

Strong debugging and observability instincts. You orient quickly in unfamiliar codebases and datasets, particularly across data pipelines with many upstream sources and downstream consumers

Computer Science Degree, or equivalent experience

Preferred Qualifications

Experience with Airflow, Dagster, Spark, Iceberg, Trino, Flink, or similar data infrastructure

Familiarity with ML infrastructure patterns (feature stores, model serving, experiment tracking)

Python Fluency

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