Lead ML Platform Engineer - Real-Time Personalization

Harnham; New York (NY); Hybrid; USD 180,000 - 240,000; Full time

Benefits offered by this job

Equity participation via RSUs

Hybrid NYC

Job summary

Harnham seeks a Lead Machine Learning Engineer to join a rapid growth ML engineering team in NYC, offering hybrid work. You will design scalable ML infrastructure, build backend services, and collaborate with Data Scientists to productionize models for real-time recommendations.

The role requires 7+ years in software or ML engineering, strong backend experience with distributed systems, and hands-on skills in Scala, AWS, Kubernetes, and CI/CD.

Qualifications

• 7+ years of software or ML engineering experience.
• Backend engineering experience building distributed systems at scale.
• Scala-based microservices and production-grade backend applications.
• Deep knowledge of AWS cloud services, including ML infrastructure.
• Hands-on with Kubernetes, Docker, Terraform, and modern CI/CD practices.

Responsibilities

• Design, build, and maintain scalable infrastructure for ML training, deployment, and inference.
• Develop and optimize backend services and cloud-native applications powering real-time ML systems.
• Own ML platform capabilities across cloud infrastructure, model serving, monitoring, and tooling.
• Collaborate with Data Scientists to productionize models for real-time recommendations.
• Improve CI/CD pipelines, IaC, observability, reliability, and scalability.
• Participate in on-call, incident response, and support for critical production services.

Skills

Backend engineering

Distributed systems

Scala

AWS

Kubernetes

Docker

CI/CD

Observability

Tools

Datadog

SageMaker

Python

Job description

Harnham seeks a Lead Machine Learning Engineer to join a rapid growth ML engineering team in NYC, offering hybrid work. You will design scalable ML infrastructure, build backend services, and collaborate with Data Scientists to productionize models for real-time recommendations.

The role requires 7+ years in software or ML engineering, strong backend experience with distributed systems, and hands-on skills in Scala, AWS, Kubernetes, and CI/CD.