AI Data Infrastructure
Build and maintain data infrastructure for AI/ML workloads, including vector databases and semantic search systems.
Design data schemas and storage solutions that support efficient retrieval and processing for LLM applications.
Contribute to data versioning, lineage tracking, and observability for AI training and inference pipelines.
Integration & Collaboration
Partner with cross-functional teams, including AI engineers, researchers, and business stakeholders, to understand data needs and design solutions.
Collaborate with infrastructure teams on cloud architecture, security, and compliance requirements.
Required Qualifications
Technical Skills
Programming: Strong proficiency in Python and SQL (experience with an additional language such as Java, Scala, or Go is a plus)
Pipeline Development: Building and orchestrating production data pipelines and ETL/ELT workflows (e.g., Apache Airflow, Prefect, Dagster)
Data Modeling & Storage: Designing schemas and working with SQL and NoSQL databases (e.g., PostgreSQL, MySQL, MongoDB, Elasticsearch)
Data Analysis & Modeling: Analyzing data and developing models using statistical and machine learning tools such as XGBoost, PyTorch, and TensorFlow; performing feature engineering, model training, and evaluation.
Cloud Platforms: Working with a major cloud provider (AWS, GCP, or Azure) and core storage and compute services
AI/ML Data: Preparing and serving data for ML/AI systems, including vector databases (e.g., Pinecone, Weaviate, Qdrant) and embedding pipelines
Preferred Experience
Financial domain knowledge — e.g., quantitative research or data science experience at a buy-side or sell-side firm
Experience supporting LLM applications or RAG (Retrieval Augmented Generation) systems
Familiarity with distributed computing frameworks (Spark, Flink) or large-scale data platforms
Familiarity with financial data sources (market data, fundamental data, alternative data)
Knowledge of data streaming technologies (Kafka, Kinesis, Pub/Sub)
Experience with data quality frameworks (Great Expectations, Deequ)
Professional Skills
Bachelor's, Master's, or PhD in Computer Science, Data Science, or a related field
Strong problem-solving skills and attention to detail
Excellent communication skills with ability to translate technical concepts for non-technical stakeholders
Experience working in fast-paced, collaborative environments
Self-motivated with ability to manage multiple priorities
Who we are Schonfeld is a global multi-manager hedge fund that strives to deliver industry-leading risk-adjusted returns for our investors. We leverage both internal and external portfolio manager teams around the world, seeking to capitalize on inefficiencies and opportunities within the markets. We draw from decades of experience and a significant investment in proprietary technology, infrastructure and risk analytics to invest across four main strategies: Quant, Tactical, Fundamental Equity and Discretionary Macro & Fixed Income.
Our Culture At Schonfeld, we’ll invest in you. Attracting and retaining top talent is at the heart of what we do, because we believe that exceptional outcomes begin with exceptional people. We foster a culture where talent is empowered to continually learn, innovate and pursue ambitious goals. We are teamwork-oriented, collaborative and encourage ideas—at all levels—to be shared. As an organization committed to investing in our people, we provide learning and educational offerings and opportunities to make an impact. We encourage community through internal networks, external partnerships and service initiatives that promote inclusion and purpose beyond the firm’s walls.
The base pay for this role is expected to be between $225k and $275k. The expected base pay range is based on information at the time this post was generated. This role may also be eligible for other forms of compensation such as a performance bonus and a competitive benefits package. Actual compensation for the successful candidate will be determined based on a variety of factors such as skills, qualifications, and experience.
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