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# Machine Learning Infrastructure Engineer, Technology

- **Company:** POINT72, L.P.
- **Location:** New York, NY, United States
- **Experience:** Experienced
- **Salary:** $185,000.0 - $300,000.0
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Microsoft Azure, C++ (Programming Language), Profiling, Software Debugging, Digital Architecture, Distributed Systems, Python (Programming Language), Key Management, Machine Learning, Reinforcement Learning, Google Cloud, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Machine Learning Operations, Terraform
- **Published:** August 4, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=7e0c71dee7aff1a9

## About the Role

* Bachelor's or master's degree in computer science, electrical engineering, or a related technical field
 * 3-7 years of experience building and maintaining scalable compute or machine learning infrastructure systems
 * Deep understanding of distributed systems, container orchestration (Kubernetes), and public cloud platforms such as AWS, Google Cloud Platform, or Azure
 * Hands-on experience with machine learning operations and infrastructure tools such as MLflow, Ray, Airflow, Kubeflow, and Terraform
 * Strong understanding of reinforcement learning concepts and their infrastructure implications
 * Proficiency in Python and systems-level programming in one or more languages such as Go, C++, or Rust
 * Strong debugging, performance profiling, and optimization skills across GPU and CPU compute stacks
 * Experience implementing monitoring, observability, and cost-optimization for GPU/accelerator-based compute environments
 * Excellent collaboration and communication skills with a systems-thinking mindset
 * Commitment to the highest ethical standards

## Description

* Design and implement high-performance infrastructure to support large-scale generative AI and machine learning workloads, enabling faster model iteration and real business impact
 * Design and operate distributed systems for model training, hyperparameter tuning, inference, and data preprocessing pipelines to deliver reliable end-to-end machine learning (ML) workflows
 * Collaborate with ML researchers and engineers to produce models, optimizing compute utilization, training throughput, and inference latency
 * Develop and automate deployment, orchestration, and CI/CD pipelines for models and data workflows using container orchestration and infrastructure-as-code (IaC)
 * Implement observability, monitoring, and cost-management strategies for GPU and accelerator compute environments to maintain predictable performance and spend
 * Evaluate, integrate, and benchmark emerging hardware and software technologies across cloud and on-prem environments to improve scalability and throughput
 * Drive security, compliance, and operational runbooks for GenAI infrastructure including access controls, secrets management, and incident response procedures
 * Troubleshoot, profile, and optimize performance across GPU and CPU compute stacks to remove bottlenecks and increase reliability
 * Document architecture, operational practices, and mentor engineers to expand team capability and accelerate adoption of production-ready GenAI infrastructure


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