Über
Point72 is a leading global alternative investment firm that seeks to deliver superior returns for its investors. In this role, you will design and build critical infrastructure for the machine learning lifecycle, collaborating with cross-functional teams to implement robust data processing pipelines and MLOps frameworks.
Responsibilities : • Architect and implement the full lifecycle of ML models, from data ingestion to production inference, contributing to the design of our next-generation, event-driven architecture, using technologies like gRPC, Kafka, and high-performance API frameworks, like FastAPI, Spring WebFlux, and Axum. • Engineer and automate robust, large-scale data processing pipelines (ETL/ELT) using tools like Spark, dbt, and workflow orchestrators, and lead the design and implementation of our Feature Store strategy. • Own the MLOps framework for model training, versioning, and deployment, including CI/CD pipelines, automated workflows, and experiment tracking and evaluation tooling. • Implement sophisticated deployment strategies, including canary, blue-green, shadow, and A/B testing, to ensure safe, zero-downtime releases, and optimize inference performance for LLMs and other large models. • Leverage cutting-edge tools and techniques like quantization and compilation to maximize throughput and minimize latency. • Collaborate with data scientists to develop models and optimize model performance for low-latency serving using techniques like Python performance tuning. • Define, provision, and manage our cloud infrastructure using Terraform, working hands-on with a wide array of cloud services across compute, storage, and machine learning platforms.
Qualifications : Required : • 5+ years of experience in a software, data, or ML engineering role. • Strong proficiency in SQL. • Experience building and orchestrating data pipelines using tools such as Spark, dbt, and Dagster/Airflow, as well as data warehouses like Snowflake, Redshift, BigQuery. • Understanding of infrastructure as code, including experience with Terraform. • Proficiency with containerization and orchestration including Docker and Kubernetes. • Hands-on experience with CI/CD tools and ML lifecycle tools, including Jenkins, MLflow, Kubeflow, and W&B. • Experience with AWS and its core services including S3, EC2, Lambda, RDS, and EMR, and practical experience with Boto3 and AWS ML services SageMaker and Bedrock. • Understanding of modern ML models and ability to discuss the performance characteristics and engineering trade-offs that influence deployment decisions. • Experience with systems incorporating Graph Neural Networks (GNNs), recommendation systems, anomaly detection, and complex time-series models. • Commitment to the highest ethical standards.
Company :
Point72 invests in multiple asset classes and strategies worldwide. Founded in 2012, the company is headquartered in Stamford, USA, with a team of 1001-5000 employees. The company is currently Late Stage.
Sprachkenntnisse
- English
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