Machine Learning Engineer

Role and Responsibilities

Here’s What You’ll Be Responsible For:

• 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.

Skills and Qualifications

Here's what you'll need:

Required

• 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.

Preferred

• 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.

The current base salary range for this role is between $90,000 - $174,500. Individual base pay rates will depend on factors including duties, work location, education, skills, qualifications and experience. Total compensation for this position will include a competitive benefits package and may include participation in company incentive compensation programs, which are based on factors to include organizational and individual performance.

Total Rewards
At Samsung Austin Semiconductor, base pay is just one part of our total compensation package. The base compensation for this role will depend on education, experience, skills, and location.

We offer a comprehensive benefits package, including:

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 also receive MBO bonuses based on company, division, and individual performance.

All positions at Samsung Austin Semiconductor are full-time on-site.

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.

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