Machine Learning Software Engineer

A leading global hedge fund with over 40+ billion in AUM and a long track record across fundamental and systematic strategies. The firm invests across asset classes and geographies, with a strong focus on building high‑performing teams and developing talent over the long term.

You’ll join their Knowledge Graph Intelligence Team focused on building intelligent systems powered by modern machine learning and graph-based technologies. The group partners closely with product managers, engineers, and data scientists to develop scalable platforms that support advanced analytics and decision‑making.

The team operates at the intersection of data, infrastructure, and AI—leveraging open‑source tools, cloud‑native architecture, and distributed systems to push forward next‑generation ML capabilities.

This is a highly data‑focused engineering role centered on building and scaling ML infrastructure end‑to‑end. You will:

• Design and build systems that support the full ML lifecycle, from data ingestion and feature engineering to model training, deployment, and monitoring
• Develop event‑driven architecture using technologies like Kafka, gRPC, and modern API frameworks (e.g., FastAPI, Spring Web Flux, Axum)
• Build and scale robust data pipelines (ETL/ELT) using tools like Spark, dbt, and workflow orchestrators
• Help define and implement a Feature Store strategy
• Own and improve MLOps workflows including model versioning, CI/CD, experiment tracking, and evaluation
• Optimize inference performance for large‑scale models, including LLMs, with a focus on latency and throughput
• Partner with data scientists to product ionize models and improve performance through efficient engineering
• Manage infrastructure using Terraform across cloud‑based environments

• 4+ years of experience in software engineering, data engineering, or ML engineering
• Experience building and orchestrating data pipelines (e.g., Spark, dbt, Airflow/Dagster) and working with modern data warehouses (Snowflake, Redshift, Big Query)
• Experience with infrastructure as code (Terraform)
• Strong experience with Docker and Kubernetes
• Hands‑on experience with CI/CD and ML tooling (e.g., Jenkins, MLflow, Kubeflow, Weights & Biases)
• Experience working with AWS (S3, EC2, Lambda, RDS, EMR), including programmatic interaction (Boto3) and ML services (e.g., Sage Maker)
• Solid understanding of ML systems and trade‑offs in production environments
• Experience working with advanced ML use cases such as recommendation systems, anomaly detection, graph‑based models, or time‑series systems

• Generous parental leave
• 401(k) with employer match
• Wellness programs (mental & physical)
• Employee resource groups and community initiatives