Job description

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. 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. 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 REQUIREMENTS - 8+ 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 “FRIENDS OF VOLEON” CANDIDATE REFERRAL PROGRAM If you have a great candidate in mind for this role and would like to have the potential to earn $15,000 if your referred candidate is successfully hired and employed by The Voleon Group, please use this form voleon.com to submit your referral. For more details regarding eligibility, terms and conditions please make sure to review the Voleon Referral Bonus Program voleon.com. EQUAL OPPORTUNITY EMPLOYER The Voleon Group is an Equal Opportunity employer. Applicants are considered without regard to race, color, religion, creed, national origin, age, sex, gender, marital status, sexual orientation and identity, genetic information, veteran status, citizenship, or any other factors prohibited by local, state, or federal law.

Requirements

Must-haves

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

Nice-to-haves

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

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningmlapipython

Platform: ashby
Effort: quick