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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. As a Software Engineer in Strategy Research Analytics, you will lead the design, evolution, and long-term architecture of Voleon’s analytics infrastructure supporting research reporting and analysis across strategies. You will own critical recurring analytics pipelines and foundational datasets, while guiding the transition from fragmented, bespoke workflows toward a standardized, observable, and query-native analytics platform. In addition to hands-on implementation, you will shape technical direction, establish reliability standards, and drive consolidation efforts that improve consistency, scalability, and reproducibility across research analytics systems. You’ll collaborate closely with Data Scientists, Researchers, and Data Infrastructure teams to ensure analytics systems run reliably and produce consistent, queryable datasets. This role offers strong technical ownership within a mission-critical area of the research organization, with meaningful impact on research velocity and insight generation. Your Team The Research Analytics team sits within Research Engineering and works closely with Data Scientists and Researchers across all of Voleon's core strategies. We look for brilliant people with a passion for solving problems through innovation and engineering fundamentals. You’ll work in a collaborative environment that encourages creative thinking and efficient implementation. You’ll work alongside experienced engineers recruited from leading technology companies and selected from the sharpest minds at university programs. The team’s mission is to: Stabilize existing analytics pipelines to ensure critical data is available for our data scientist and research partners Implement monitoring/alerting and operational runbooks; participate in incident response and postmortems Standardize outputs from strategy workflows into a unified analytics schema (tables, metrics definitions, partitioning strategy) Improve dataset discoverability via documentation, schema contracts, and metadata/lineage primitives Optimize query performance and cost for distributed engines (Presto/Spark) and columnar formats (Parquet/ORC) Responsibilities Own implementation and on-going operation of recurring analytics pipelines (e.g., Airflow DAGs) including monitoring, alerting, and reliability improvements Lead architectural evolution of the analytics platform, including schema standardization, DAG consolidation, and modernization of legacy workflows Drive cross-team technical alignment when consolidating duplicated or inconsistent analytics outputs Build and maintain base analytics tables and metrics with strong schema discipline and reproducible computation Define and implement reliability standards (SLOs, observability patterns, runbooks) adopted across analytics pipelines Improve transparency and usability through documentation, discoverability, and clear data contracts Optimize distributed compute and SQL query performance; design data layouts (partitioning, file sizing) for columnar storage Mentor engineers through design reviews and raise the bar for operational and modeling rigor Requirements Bachelor’s degree in Computer Science or equivalent professional experience 3+ years of experience building and operating analytics or data infrastructure systems Strong proficiency in Python and SQL Deep experience with distributed query engines and large-scale compute systems Demonstrated ownership of large-scale or mission-critical data infrastructure Strong data modeling expertise, including schema design, partitioning strategy, and reproducibility considerations Expertise in metadata management, data lineage, and applying robust data governance principles Preferred Qualifications Experience leading architectural migrations or major refactors of data platforms Familiarity with AWS cloud technologies and on-prem compute clusters (e.g., Slurm, SSH, Unix) Exposure to quantitative research or machine learning environments. “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 to submit your referral. For more details regarding eligibility, terms and conditions please make sure to review the Voleon Referral Bonus Program . 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.

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. As a Software Engineer in Strategy Research Analytics, you will lead the design, evolution, and long-term architecture of Voleon’s analytics infrastructure supporting research reporting and analysis across strategies. You will own critical recurring analytics pipelines and foundational datasets, while guiding the transition from fragmented, bespoke workflows toward a standardized, observable, and query-native analytics platform. In addition to hands-on implementation, you will shape technical direction, establish reliability standards, and drive consolidation efforts that improve consistency, scalability, and reproducibility across research analytics systems. You’ll collaborate closely with Data Scientists, Researchers, and Data Infrastructure teams to ensure analytics systems run reliably and produce consistent, queryable datasets. This role offers strong technical ownership within a mission-critical area of the research organization, with meaningful impact on research velocity and insight generation. Your Team The Research Analytics team sits within Research Engineering and works closely with Data Scientists and Researchers across all of Voleon's core strategies. We look for brilliant people with a passion for solving problems through innovation and engineering fundamentals. You’ll work in a collaborative environment that encourages creative thinking and efficient implementation. You’ll work alongside experienced engineers recruited from leading technology companies and selected from the sharpest minds at university programs. The team’s mission is to: Stabilize existing analytics pipelines to ensure critical data is available for our data scientist and research partners Implement monitoring/alerting and operational runbooks; participate in incident response and postmortems Standardize outputs from strategy workflows into a unified analytics schema (tables, metrics definitions, partitioning strategy) Improve dataset discoverability via documentation, schema contracts, and metadata/lineage primitives Optimize query performance and cost for distributed engines (Presto/Spark) and columnar formats (Parquet/ORC) Responsibilities Own implementation and on-going operation of recurring analytics pipelines (e.g., Airflow DAGs) including monitoring, alerting, and reliability improvements Lead architectural evolution of the analytics platform, including schema standardization, DAG consolidation, and modernization of legacy workflows Drive cross-team technical alignment when consolidating duplicated or inconsistent analytics outputs Build and maintain base analytics tables and metrics with strong schema discipline and reproducible computation Define and implement reliability standards (SLOs, observability patterns, runbooks) adopted across analytics pipelines Improve transparency and usability through documentation, discoverability, and clear data contracts Optimize distributed compute and SQL query performance; design data layouts (partitioning, file sizing) for columnar storage Mentor engineers through design reviews and raise the bar for operational and modeling rigor Requirements Bachelor’s degree in Computer Science or equivalent professional experience 3+ years of experience building and operating analytics or data infrastructure systems Strong proficiency in Python and SQL Deep experience with distributed query engines and large-scale compute systems Demonstrated ownership of large-scale or mission-critical data infrastructure Strong data modeling expertise, including schema design, partitioning strategy, and reproducibility considerations Expertise in metadata management, data lineage, and applying robust data governance principles Preferred Qualifications Experience leading architectural migrations or major refactors of data platforms Familiarity with AWS cloud technologies and on-prem compute clusters (e.g., Slurm, SSH, Unix) Exposure to quantitative research or machine learning environments. “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 to submit your referral. For more details regarding eligibility, terms and conditions please make sure to review the Voleon Referral Bonus Program . 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.

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. The people who shape our company come from other backgrounds, including concert music performances, humanitarian aid, opera singing, sports writing, and BMX racing. You will be part of a team that loves to succeed together. 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. As a Site Reliability Engineer (SRE), you will work at the intersection of production operations and software development as you improve, manage, and monitor production-critical infrastructure and data pipelines. At Voleon, many SREs serve together on a Production Operations team tasked with improving shared production infrastructure. Others are embedded with teams of software engineers to improve specific production systems owned by those teams. Voleon SREs work on important real-world problems and collaborate with passionate and talented colleagues in an empowering, results-driven environment. This role is a way to make a real difference: your contributions will make our critical systems more reliable, lower operational risk, and increase the efficiency of our engineering effort. Responsibilities Improve fault-tolerance and maintainability of code in proprietary data pipelines and trading systems Diagnose and fix bugs in code Lead complex deployments Automate manual workflows Track and prioritize outstanding production-related issues Share an on-call rotation responding to incidents to ensure the continuous operation of production-critical systems Requirements Experience with coding and debugging Python Experience with Linux Familiarity with Relational Databases & SQL Sharp analytical and problem-solving skills and a persistent drive to make things work (better) Strong growth mindset and a passion for learning Strong technical communication skills Attention to detail 2 years of relevant industry experience An undergraduate degree or comparable training in a quantitative field or equivalent, relevant industry experience Preferred Qualifications Familiarity with best practices concerning code maintainability, documentation, quality assurance, continuous integration and deployment Experience supporting production systems Experience with any of the following: gRPC microservices, Postgres, Pandas, Golang, R, Git, Jenkins, Bazel, Prometheus, Grafana, Airflow, Kubernetes “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 $7,500 if your referred candidate is successfully hired and employed by The Voleon Group, please use this form to submit your referral. For more details regarding eligibility, terms and conditions please make sure to review the Voleon Referral Bonus Program . 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.

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. As a Senior Machine Learning Engineer on one of Voleon's Research teams, you will partner directly with research staff to advance our quantitative trading strategies. You will translate novel research ideas into production-quality code, build and maintain the data pipelines and modeling infrastructure that underpin our strategies, and apply your own strong mathematical intuition to solve open-ended technical challenges. This role lives at the boundary of research and engineering. You will be expected to understand the statistical and mathematical concepts your research partners work with, contribute meaningfully to technical discussions about model design and evaluation, and ensure that the resulting systems are performant, reliable, and maintainable. You will work at the intersection of Computer Science, Mathematics, and Statistics — building high-performance tools that enable world-class research while maintaining a high engineering standard. Responsibilities Partner with PhD researchers to design, implement, and productize machine learning models that drive quantitative trading strategies Develop and maintain complex data pipelines, including data ingestion, feature engineering, validation, and quality monitoring Translate research prototypes and novel ideas into performant, well-tested, production-ready code Build extensible tools and frameworks that accelerate the model development and experimentation lifecycle Supervise, understand, and remediate subtle data quality issues across both research and production environments Proactively lead projects from requirements through delivery, making autonomous decisions about scope, dependencies, and trade-offs, with an emphasis on long-term maintainability Coordinate and contribute to deployment efforts while guiding junior engineers and researchers; align with research and engineering stakeholders on ownership, execution, and prioritization Foster engineering consistency, standards, and best practices within Research Requirements Bachelor's degree (or higher) in Computer Science, Applied Mathematics, Statistics, or a related quantitative field 5+ years of professional software engineering experience, with strong CS fundamentals (data structures, algorithms, systems design) Demonstrated mathematical maturity — comfort with the concepts and notation used in statistics, linear algebra, optimization, and probability Deep proficiency in Python; experience with R and/or C/C++ is a strong plus Extensive experience with numerical and data science libraries (e.g., NumPy, Pandas, SciPy, scikit-learn, PyTorch, TensorFlow, or similar) Proven experience building or maintaining machine learning systems in a distributed computing environment Proficiency developing in a Linux environment with attention to performance, correctness, and reproducibility Exceptional attention to detail, particularly when working with imperfect or heterogeneous data Strong verbal and written communication skills, and the ability to collaborate effectively with researchers whose primary expertise is not software engineering Preferred Qualifications Experience with experiment management, model evaluation pipelines, or ML workflow orchestration Familiarity with modern ML/AI infrastructure patterns (model serving, feature stores, distributed training) Experience with performance profiling and optimization of numerical or modeling code Prior exposure to financial data, time-series analysis, or quantitative research environments “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 to submit your referral. For more details regarding eligibility, terms and conditions please make sure to review the Voleon Referral Bonus Program . 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.