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# Lead Machine Learning Engineer

- **Company:** Harnham Inc.
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $200,000.0 - $220,000.0
- **Contract:** Permanent contract
- **Skills:** Cloud Computing, Continuous Integration, Distributed Systems, Python (Programming Language), Machine Learning, Recommender Systems, Cloud Services, Azure Machine Learning, Software Engineering, Datadog, Spring Cloud, Backend, Kubernetes, Machine Learning Operations, Terraform, Docker, Microservices
- **Published:** August 31, 2026
- **Apply:** https://www.disabledperson.com/jobs/74613322-lead-machine-learning-engineer

## About the Role

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.
 * Strong Python programming skills.
 * 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.

## Description

* 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.


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