Senior Software Engineer, Strategy Platform
The Voleon Group
Voleon is a technology company that applies state-of-the-art AI and machine learning techniques to real-world problems in finance. For nearly two decades, we have led our industry and worked at the frontier of applying AI/ML to investment management. We have become a multibillion-dollar asset manager, and we have ambitious goals for the future.
Your colleagues will include internationally recognized experts in artificial intelligence and machine learning research as well as highly experienced finance and technology professionals. In addition to our enriching and collegial working environment, we offer highly competitive compensation and benefits packages, technology talks by our experts, a beautiful modern office, daily catered lunches, and more.
Responsibilities 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
- 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
- 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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