GenAI ML Infra Engineer

Point72; New York (NY); On-site; USD 185,000 - 300,000; Full time

Benefits offered by this job

Fully paid health care

Parental and family leave

Wellness programs

401(k) with employer match

Job summary

Point72’s Technology team reimagines the future of investing, building AI‑powered platforms with open‑source tools and modern cloud architectures. This role designs high‑performance infrastructure for generative AI and ML workloads, enabling rapid model iteration and reliable end‑to‑end workflows across cloud and on‑prem environments.

You will collaborate with ML researchers to optimize training, inference, and data pipelines, and will develop automation for deployment, orchestration, and CI/CD.

Qualifications

• Bachelor's or master's degree in CS, EE, or related field.
• 3–7 years building scalable compute or ML infrastructure.
• Experience with distributed systems, Kubernetes, and public clouds.
• Proficiency in Python and systems languages (Go, C++, or Rust).

Responsibilities

• Design and implement high‑performance infrastructure for large‑scale generative AI workloads.
• Design and operate distributed ML training, tuning, inference pipelines.
• Collaborate with ML researchers to optimize compute utilization, training throughput, and inference latency.
• Develop and automate deployment, orchestration, and CI/CD pipelines for models and data workflows using IaC.
• Implement observability, monitoring, and cost management for GPU and accelerator compute environments.
• Benchmark emerging hardware and software technologies to improve scalability.

Skills

Distributed systems

Python

Go

Rust

ML infra

Observability

Cost optimization

Collaboration

Ethical standards

ML infra

Education

Bachelor's or master's degree in CS/EE or related field

Tools

Kubernetes

AWS

GCP

Azure

MLflow

Ray

Airflow

Kubeflow

Terraform

Docker

Job description

Point72’s Technology team reimagines the future of investing, building AI‑powered platforms with open‑source tools and modern cloud architectures. This role designs high‑performance infrastructure for generative AI and ML workloads, enabling rapid model iteration and reliable end‑to‑end workflows across cloud and on‑prem environments.

You will collaborate with ML researchers to optimize training, inference, and data pipelines, and will develop automation for deployment, orchestration, and CI/CD.