Machine Learning Platform Engineer at QuinStreet

Monterrey, Chiapas, Mexico -

Full Time

Start Date

Immediate

Expiry Date

30 Dec, 25

Salary

0.0

Posted On

01 Oct, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Infrastructure, Data Pipelines, Feature Management, Cloud Platforms, Containerization, Orchestration, CI/CD, MLOps, Distributed Training, Python, Java, Go, Scala, Unix/Linux, Scripting

Industry

technology;Information and Internet

Description

Powering Performance Marketplaces in Digital Media QuinStreet is a pioneer in powering decentralized online marketplaces that match searchers and “research and compare” consumers with brands. We run these virtual- and private-label marketplaces in one of the nation’s largest media networks. Our industry leading segmentation and AI-driven matching technologies help consumers find better solutions and brands faster. They allow brands to target and reach in-market customer prospects with pinpoint segment-by-segment accuracy, and to pay only for performance results. Our campaign-results-driven matching decision engines and optimization algorithms are built from over 20 years and billions of dollars of online media experience. We believe in: The direct measurability of digital media. Performance marketing. (We pioneered it.) The advantages of technology. We bring all this together to deliver truly great results for consumers and brands in the world’s biggest channel. Job Category We are looking for a talented and driven Machine Learning Platform Engineer to join our growing ML team in Mexico. In this role, you will be responsible for building, deploying, and maintaining robust and scalable machine learning infrastructure and pipelines. You will work closely with data scientists, data engineers, and software developers to ensure models are production-ready, secure, and deliver real business value. This role will be based in Mexico. Responsibilities Design and maintain scalable infrastructure for model training, serving, monitoring, and feature management. Build and manage data pipelines, feature stores, and metadata stores to support ML workflows. Optimize memory and compute efficiency for large-scale training and inference. Enable distributed training and deployment across heterogeneous hardware (CPU, GPU, etc.). Automate end-to-end ML workflows using orchestration tools like Airflow or Argo. Ensure high availability, observability, and reliability of ML systems in production. Collaborate with ML engineers, data scientists, and infrastructure teams to streamline development and deployment. Enforce security, compliance, and infrastructure best practices throughout the ML stack. Requirements Bachelor’s or Master’s degree in Computer Science, Engineering, or related field. 3+ years of experience in backend, infrastructure, or ML systems engineering. Proficient in Python and at least one backend language (Java, Go, or Scala). Hands-on experience with datastores (SQL, NoSQL), feature stores, and distributed data processing (e.g., Spark, Kafka). Experience with cloud platforms, containerization (Docker), orchestration (Kubernetes), and CI/CD for ML workflows. Experience with MLOps tools (MLflow, Kubeflow) and distributed training frameworks (e.g., PyTorch). Strong foundation in Unix/Linux systems and scripting. Preferred Qualifications Experience scaling ML infrastructure in production environments. Knowledge of model optimization and hardware-aware deployment strategies. Experience with MLOps tools (MLflow, Kubeflow) Unix and Python experience Nice to Have Familiarity with Marketplaces in AdTech/MarTech domains #LI-REMOTE QuinStreet is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, national origin, pregnancy status, sex, age, marital status, disability, sexual orientation, gender identity or any other characteristics protected by law. Please see QuinStreet’s Employee Privacy Notice here.

Responsibilities

The Machine Learning Platform Engineer will design and maintain scalable infrastructure for model training, serving, monitoring, and feature management. They will collaborate with data scientists and engineers to ensure models are production-ready and deliver business value.