Machine Learning Engineer

Samsung; Taylor (TX); On-site; USD 90,000 - 175,000; Full time

Benefits offered by this job

Medical, dental, and vision insurance

401(k) matching with immediate vesting

Onsite café and workout facilities

Paid maternity and paternity leave

PTO + 2 personal holidays and 10 fixed

Wellness incentives and MORE

MBO bonuses based on performance

Job summary

Samsung Austin Semiconductor in Taylor, TX is seeking a Machine Learning Engineer to build and maintain production ML pipelines for anomaly detection and root cause analysis on onsite infrastructure. You will develop PySpark workflows to ingest and transform high-volume manufacturing data, optimize Spark performance, and deploy end-to-end ML pipelines with reproducible runs and auditable results.

Strong experience with PySpark, distributed data processing, Python ML libraries, and MLOps is

Qualifications

• Bachelor’s degree or higher in a quantitative field.
• 3-5+ years of experience building and maintaining ML systems.
• Strong PySpark and distributed data processing skills.

Responsibilities

• Develop PySpark workflows to ingest, clean, and transform high-volume data.
• Tune Spark performance: partition strategies, memory, and joins.
• Create end-to-end ML pipelines with reproducible runs and auditable results.
• Build models for anomaly detection and root cause analysis.
• Manage production model lifecycle, versions, retraining, and rollbacks.
• Monitor pipeline timings, data quality, and model metrics with alerts.

Skills

PySpark

Distributed data processing

Python ML libraries

MLOps

Monitoring and alerting

Education

Bachelor’s degree or higher in CS/SE/Data Science

Tools

Spark

Python

scikit-learn

TensorFlow

PyTorch

XGBoost

Job description

Samsung Austin Semiconductor is seeking a Machine Learning Engineer (onsite in Taylor, TX) to build and maintain production ML pipelines for anomaly detection and root cause analysis.

Responsibilities

• Develop PySpark workflows to ingest, clean, and transform high-volume manufacturing data into structured datasets for training and inference.
• Improve performance of Spark jobs by tuning partition strategies, managing executor memory, reducing shuffle operations, and addressing skewed joins to lower runtime and cluster resource usage.
• Create and maintain end-to-end ML pipelines that automate feature calculation, model training, validation, and deployment with runs that are reproducible and auditable.
• Build and tune machine learning models for anomaly detection and root cause analysis.
• Manage model lifecycle in production by tracking model versions, storing artifacts securely, triggering automated retraining, and performing rollback procedures when performance degrades.
• Monitor pipeline execution time, data quality checks, and model metrics including accuracy, drift, and throughput; implement alerting rules to catch failures or degradation early.

Requirements

• Bachelor’s degree or higher in Computer Science, Software Engineering, Data Science, or a related quantitative field.
• 3-5+ years of professional experience building and maintaining machine learning systems.
• Strong proficiency in PySpark and distributed data processing, including experience optimizing jobs for speed and memory.
• Hands-on experience with Python ML libraries including one or more of: scikit-learn, TensorFlow, PyTorch, XGBoost for training and evaluation.
• Practical knowledge of MLOps, including pipeline orchestration, model versioning, experiment tracking, and deployment.
• Experience setting up monitoring and alerting for data pipelines and deployed models.

Technologies

• PySpark
• Spark
• Python
• scikit-learn
• TensorFlow
• PyTorch
• XGBoost

Benefits

• Medical, dental, and vision insurance
• Life insurance and 401(k) matching with immediate vesting
• Onsite café(s) and workout facilities
• Paid maternity and paternity leave
• Paid time off (PTO) + 2 personal holidays and 10 regular holidays
• Wellness incentives and MORE
• Eligible full-time employees (salaried or hourly) may receive MBO bonuses based on company, division, and individual performance

Preferred

• Experience setting up model registries, automated retraining triggers, and rollback procedures to support reliable production models.
• Experience writing automated tests and validation checks for data pipelines and model outputs to catch errors before deployment.
• Familiarity with on-prem or private cloud infrastructure, including cluster management and secure artifact storage.

Compensation and Work Model

• Base pay range: $90,000 - $174,500 per year
• Work model: Full-time, on-site at Samsung Austin Semiconductor

U.S. Export Control Compliance

• This role may require access to information subject to U.S. export control laws; applicants must be authorized to access such information or eligible for government authorization.

Trade Secrets Notice

• By submitting an application, you agree not to disclose to Samsung, or encourage Samsung to use, any confidential or proprietary information (including trade secrets) belonging to a current or former employer or other entity.