Lead ML Platform Engineer

Harnham; New York (NY); Hybrid; USD 150,000 - 190,000; Full time

Benefits offered by this job

Competitive base salary and annual be

Equity participation through RSUs

Opportunity to work on cutting-edge AI

Career growth and technical leadership

Exposure to large-scale real-time prod

Job summary

Harnham is partnering with a leading consumer technology and financial services organization to advance a large-scale ML platform in New York. You will design, build, and operate distributed backend services powering real-time recommendation and personalization at scale.

Join a growing ML engineering team focusing on production-grade infrastructure, cloud-native deployments, and robust observability. This role offers significant leadership opportunities and exposure to cutting-edge AI systems.

Qualifications

• 7+ years of software or ML engineering experience.
• Backend engineering with distributed systems experience.
• Scala-based microservices and production-grade backend apps.
• Deep knowledge of AWS cloud services.
• Hands-on with Kubernetes, Docker, Terraform, and CI/CD practices.
• Track record of owning production systems, reliability, and monitoring.

Responsibilities

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

Skills

Scala microservices

Distributed systems

AWS cloud

CI/CD

Reliability & observability

Production systems ownership

Tools

Kubernetes

Docker

Terraform

Job description

THE COMPANY

We are partnering with a leading consumer technology and financial services organization that operates at global scale and serves hundreds of millions of users. The business leverages advanced data, machine learning, and real-time decisioning systems to deliver highly personalized customer experiences across a broad portfolio of digital products.

This is an exciting opportunity to join a rapidly expanding machine learning engineering team at a pivotal stage of growth. The organization is investing heavily in recommendation systems, real-time personalization, machine learning platforms, and next-generation AI capabilities, offering engineers the opportunity to work with large-scale distributed systems and production-grade ML infrastructure.

RESPONSIBILITIES

• Design, build, and maintain scalable infrastructure supporting machine learning training, deployment, and inference workloads.
• Develop and optimize backend services, microservices, and cloud-native applications that power real-time machine learning systems.
• Own and enhance ML platform capabilities across cloud infrastructure, model serving, monitoring, and operational tooling.
• Partner closely with Data Scientists to productionize machine learning models and support real-time recommendation and personalization use cases.
• Improve CI/CD pipelines, infrastructure-as-code frameworks, observability, reliability, and system scalability.
• Participate in operational ownership, incident response, and support for critical production services.

SKILLS AND EXPERIENCE

Must-Have

• 7+ years of software engineering or machine learning engineering experience.
• Strong backend engineering expertise with experience building distributed systems at scale.
• Proven experience developing Scala-based microservices and production-grade backend applications.
• Deep knowledge of AWS cloud services, including machine learning infrastructure and managed platforms.
• Hands-on experience with Kubernetes, Docker, Terraform, and modern CI/CD practices.
• Track record of owning production systems, reliability, monitoring, and operational excellence.

Nice-to-Have

• Experience with machine learning infrastructure, MLOps, or model-serving platforms.
• Knowledge of recommendation systems, personalization engines, or CTR optimization.
• Experience with Datadog observability and monitoring.
• Background in adtech, fintech, e-commerce, or other high-scale consumer platforms.
• Exposure to real-time machine learning applications and online inference systems.

BENEFITS

• Competitive base salary and annual bonus
• Equity participation through RSUs
• Opportunity to work on cutting-edge AI and machine learning initiatives
• Significant career growth and technical leadership opportunities
• Exposure to large-scale, real-time production systems