Machine Learning Engineer

Samsung Electronics•Taylor, TX

1d•$90,000 - $174,500•Onsite

About The Position

As a Machine Learning Engineer at Samsung Austin Semiconductor, you will build and maintain the model pipelines for our anomaly detection and root cause analysis systems. You will work heavily with PySpark to process large-scale time-series and operational data, prepare training datasets, and manage the full model deployment lifecycle. Your day-to-day will involve designing robust ML pipelines, developing and validating models, and optimizing PySpark jobs for large-scale processing. You will bridge the gap between model development and production, contributing to model tuning while taking ownership of the scalable systems that bring these models to life. The team operates in a collaborative, sprint-driven environment where you will have the autonomy to design technical approaches, test new tools, and iterate quickly based on feedback. Prior semiconductor experience is helpful but not required; you will learn the domain through hands-on projects and direct support from the team.

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, with experience optimizing jobs for speed and memory.
Hands-on experience with Python ML libraries (scikit-learn, TensorFlow, PyTorch, or XGBoost) for model training and evaluation.
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.

Nice To Haves

Experience setting up model registries, automated retraining triggers, and rollback procedures to keep production models reliable.
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.
Prior semiconductor experience

Responsibilities

Build PySpark workflows that ingest, clean, and transform high-volume manufacturing data, converting raw signals into structured datasets ready for training and inference.
Optimize Spark jobs by tuning partition strategies, managing executor memory, minimizing shuffle operations, and handling skewed joins to reduce runtime and cluster resource usage.
Design and maintain end-to-end ML pipelines that automate feature calculation, model training, validation, and deployment, ensuring each run is reproducible and auditable.
Implement and tune machine learning models for anomaly detection and root cause analysis.
Manage the model lifecycle in production: track versions, store artifacts securely, trigger automated retraining, and execute rollback procedures when performance degrades.
Monitor pipeline execution times, data quality checks, and model metrics (accuracy, drift, throughput), building alerting rules to catch failures or degradation early.