Machine Learning Software Engineer

Software Engineer, Machine Learning
New York

About the Firm
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.

The Team
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.

What You’ll Do
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 WebFlux, 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
Implement production-grade deployment strategies (canary, blue-green, A/B testing, shadow deployments)
Optimize inference performance for large-scale models, including LLMs, with a focus on latency and throughput
Partner with data scientists to productionize models and improve performance through efficient engineering
Manage infrastructure using Terraform across cloud-based environments

Requirements
4+ years of experience in software engineering, data engineering, or ML engineering
Strong SQL skills
Experience building and orchestrating data pipelines (e.g., Spark, dbt, Airflow/Dagster) and working with modern data warehouses (Snowflake, Redshift, BigQuery)
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., SageMaker)
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

What They Offer
Comprehensive health benefits
Generous parental leave
401(k) with employer match
Wellness programs (mental & physical)
Tuition reimbursement
Employee resource groups and community initiatives
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