Machine Learning Engineer

Samsung Electronics

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: Austin, TX
Salary: $90,000–$174,500 / yr
Posted: 28 days ago
Freshness: Confirmed live yesterday
Closes: Jan 1, 2027
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $223k

This role $132k

$69k most similar roles pay here $286k

This role pays less than 96% of similar roles. Most pay $192,050–$254,750 — the shaded band above. At the midpoint, this role pays about $132k versus about $223k for comparable roles.

Based on 240 similar postings.

Employer

About Samsung Electronics

Samsung Electronics is a South Korean multinational corporation and a global leader in technology, specializing in consumer electronics, semiconductors, and home appliances.

Samsung Electronics currently has 94 open roles on FindRole.

Listed pay typically runs $106,000–$174,500 across 58 roles with salary data.

Most-posted roles

View all roles at Samsung Electronics

At a glance

TL;DR · Machine Learning Engineer

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As a Machine Learning Engineer at Samsung Austin Semiconductor, you will join a collaborative team to build and maintain model pipelines for anomaly detection and root cause analysis systems. You will be responsible for developing end-to-end machine learning pipelines that automate feature calculation, training, validation, and deployment while managing the full model lifecycle in production. Your daily work involves processing high-volume manufacturing data by building PySpark workflows to transform raw signals into structured datasets. To succeed, you must possess strong proficiency in PySpark for distributed data processing and experience with Python libraries such as scikit-learn, TensorFlow, PyTorch, or XGBoost. You will also utilize MLOps practices for model versioning, experiment tracking, and monitoring. This role focuses on solving technical challenges related to manufacturing data analysis and ensuring the reliability of scalable production systems.

What does a Machine Learning Engineer earn?

Median $236000 from 319 postings across 54 companies.

See salary data

Skills

PySpark Python Machine Learning MLOps TensorFlow PyTorch scikit-learn XGBoost Distributed Data Processing Model Deployment Pipeline Orchestration Anomaly Detection Root Cause Analysis Cloud Infrastructure Monitoring and Alerting

What you'll do

Build PySpark workflows to ingest, clean, and transform high-volume manufacturing data into structured training datasets.
Optimize Spark jobs by tuning partition strategies, memory management, and shuffle operations to reduce resource usage.
Design and maintain end-to-end ML pipelines that automate feature calculation, model training, validation, and deployment.
Implement and tune machine learning models specifically for anomaly detection and root cause analysis.
Manage the full model lifecycle including versioning, artifact storage, automated retraining, and rollback procedures.
Monitor pipeline execution times, data quality, and model metrics while establishing automated alerting rules.
Develop automated tests and validation checks to identify errors in data pipelines before deployment.

What we're looking for

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 with experience optimizing jobs for speed and memory.
Hands-on experience with Python ML libraries such as scikit-learn, TensorFlow, PyTorch, or XGBoost.
Practical knowledge of MLOps practices including pipeline orchestration, model versioning, experiment tracking, and deployment.
Experience setting up monitoring and alerting for both data pipelines and deployed models.
Familiarity with on-prem or private cloud infrastructure, including cluster management and secure artifact storage.
Authorization to access information subject to U.S. export control laws.