ML Engineer / ML Platform Engineer

Tap Growth ai; Texas; Hybrid; USD 120,000 - 180,000; Full time

Job summary

Tap Growth ai is hiring an experienced ML Engineer / ML Platform Engineer to design, build, and maintain scalable ML platforms and personalization solutions in a primarily remote role across Austin, San Antonio, or Dallas areas.

The role focuses on hands-on ML engineering, MLOps, and data platform development, with opportunities to influence AI initiatives across digital commerce experiences.

Qualifications

• Hands-on ML engineer with expertise across machine learning and data engineering.
• Experience with large-scale distributed systems and MLOps practices.
• Ability to design and deploy production ML models and pipelines.

Responsibilities

• Develop and deploy recommendation models, ranking models, and personalization services.
• Build real-time and batch ML pipelines for customer behavior and model training.
• Manage feature pipelines and monitor model performance in production.
• Collaborate with product managers, data scientists, and engineers to deliver personalization capabilities.
• Conduct A/B testing to optimize customer outcomes.

Tools

Python

Spark

Databricks

Kafka

TensorFlow

PyTorch

MLOps

Model Deployment

Feature Engineering

Job description

We’re Hiring: ML Engineer / ML Platform Engineer!

We are seeking an experienced and innovative ML Engineer / ML Platform Engineer to join our team in Dallas. The ideal candidate will have a strong background in designing, building, and maintaining scalable machine learning platforms and solutions. Bring your expertise to drive impactful AI initiatives in a collaborative, fast-paced environment.

Senior ML Engineer / ML Platform Engineer

Location: Remote but needs to live in Austin, San Antonio and Dallas

Open to: Austin, San Antonio or Dallas, TX offices – can be mostly remote but needs to live in one of those areas to go into meetings occasionally AND if they convert to FTE, there will be return to office 5 days a week starting Sept 2027

Overview

Hands-on ML engineer responsible for building recommendation pipelines, deploying machine learning models, engineering data platforms, and supporting personalization initiatives at scale.

H-E-B is seeking a Senior Machine Learning Engineer to build and scale personalization, recommendation, and ranking capabilities supporting digital commerce initiatives. This engineer will develop machine learning pipelines, deploy production models, and help deliver next-generation customer experiences across web and mobile channels.

The ideal candidate is a hands-on engineer with expertise across machine learning, data engineering, MLOps, and large-scale distributed systems.

Key Responsibilities

• Develop and deploy:
• Recommendation models
• Ranking models
• Customer segmentation models
• Propensity models
• Personalization services
• Build real-time recommendation systems leveraging streaming customer events.
• Create large-scale batch processing pipelines for customer behavior analysis and model training.
• Engineer and manage feature pipelines for production ML systems.
• Implement MLOps best practices including CI/CD, monitoring, retraining, testing, and governance.
• Partner with product managers, data scientists, and engineers to launch new personalization capabilities.
• Conduct A/B testing and experimentation to optimize customer outcomes.
• Deploy models into production environments and monitor model performance.
• Support cloud modernization initiatives and migration of ML workloads.

Required Technology Stack

• Python
• Spark
• Databricks
• Kafka
• TensorFlow and/or PyTorch
• MLOps
• Model Deployment
• Feature Engineering

Preferred Cloud Experience

• AWS
• BigQuery
• Vertex AI
• Pub/Sub
• Dataflow
• GKE
• Cloud Run
• AWS $B\"*(B GCP migration experience

Target Background

• 4-8 years of experience
• ML Engineering, MLOps, or Data Platform Engineering
• Recommendation systems or personalization experience
• Retail/eCommerce customer analytics preferred