Taylor, TX

Data Jobs in Taylor, TX

3 jobs found

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11d ago

Senior Machine Learning Engineer, Defect Quality & Systems

Samsung Taylor, TX

Taylor, TX

11d ago

$106k - $145k/yr Full time Senior Ai Ml Artificial Intelligence

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Job Description

Work onsite in Taylor, TX with a team focused on high-throughput, real-time defect engineering. This Senior Machine Learning Engineer role at Samsung supports in-house ML models that power defect detection and classification in a 24/7 semiconductor manufacturing environment, with ownership across the full model lifecycle and opportunities to set standards for engineering quality.

The base salary range for this position is $106,000 - $145,000 per year. The role is full-time on-site at Samsung Austin Semiconductor, and total compensation includes a competitive benefits package with potential participation in company incentive compensation programs.

What you’ll do

Lead the design and implementation of advanced ML models for defect detection and classification, optimizing for accuracy, throughput, and minimal time-to-detection.
Collaborate with expert module owners to convert complex physical defect characteristics into mathematical modeling proposals and rigorous classification frameworks.
Drive the move from research to production by scanning emerging AI research and whitepapers, then adapting new architectures to address semiconductor manufacturing challenges.
Improve model performance through feature engineering, hyperparameter tuning, and modern vision approaches such as CNNs and object detection.
Build and maintain models that support 24/7 production reliability and seamless integration.
Set the bar for codebase quality and design principles in the group, including technical mentorship and rigorous code reviews.

What you bring

BS or MS in Software Engineering, Computer Science, Data Science, Mathematics, or a related STEM/Engineering degree.
5+ years of relevant experience.
Semiconductor industry experience is highly preferred.
Proficiency with open-source ML frameworks, including TensorFlow 2.x and PyTorch, with experience deploying models into production.
Comfort working with command-line interfaces and Linux (Ubuntu), including optimizing models for hardware performance.
Ability to translate complex ML metrics into actionable insights for both engineers and site leadership.
Track record managing complex technical projects from conception through delivery while meeting strict deadlines and software design principles.

Tools and technologies

TensorFlow 2.x, PyTorch
CNNs, Object Detection
Linux, Ubuntu

Benefits

Medical, dental, and vision insurance
Life insurance and 401(k) matching with immediate vesting
Onsite cafe(s) and workout facilities
Paid maternity and paternity leave
PTO + 2 personal holidays and 10 regular holidays
Wellness incentives and MORE
MBO bonuses based on company, division, and individual performance
Competitive benefits package, which may include participation in company incentive compensation programs

Total rewards

Base salary range: $106,000 - $145,000
Total compensation includes a competitive benefits package and may include participation in company incentive compensation programs
All positions at Samsung Austin Semiconductor are full-time on-site

Trade secrets notice: By submitting an application, you agree not to disclose to Samsung or induce 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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1mo ago

Applied AI Engineer

ERCOT/Electric Reliability Council of Texas Taylor, TX

Taylor, TX

1mo ago Closed

$145k - $200k/yr Full time Agentic Ai Agentic Automation Ai Agent

This position is no longer accepting applications (closed Oct 29, 2026).

Job Description

ERCOT, the Electric Reliability Council of Texas, is seeking an Applied AI Engineer to build and deploy production generative AI capabilities in a regulated environment. The role focuses on architecting reliable systems, including agentic workflows and RAG pipelines, with governance, evaluation, and dependable operations.

What you’ll do

Translate ambiguous business problems into scoped technical roadmaps, identifying constraints such as data access, compliance, latency, and cost before development starts.
Design and build production agentic systems covering planning, tool-calling, multi-step reasoning, memory, and error recovery using orchestration frameworks such as LangGraph or Microsoft Agent Framework.
Implement production RAG pipelines, including chunking, embeddings, hybrid search, reranking, retrieval-quality evaluation, and content freshness.
Build and extend connectors that provide agents secure, standardized access to enterprise tools and data.
Deploy applications to managed cloud platforms and integrate them with enterprise systems and collaboration tools.
Create evaluation suites, tracing, and rollback paths so agent behavior is reliable in production, not limited to demonstrations.
Monitor, debug, and continuously improve deployed applications using evaluation metrics.
Apply system design fundamentals by defining architecture, data flows, and integration boundaries with attention to scalability, reliability, latency, and cost.
Codify repeatable patterns by turning successful builds into reusable components and reference architecture.
Work with non-technical business owners to understand workflows, maintain awareness of evolving LLM capabilities, and apply current implementation patterns and AI development stacks.

Requirements

Proven experience building and deploying production-grade autonomous agents, not prototypes.
Experience with agent orchestration frameworks such as LangGraph, Microsoft Agent Framework, or comparable tools.
Production RAG experience using vector search and vector databases such as pgvector, Azure AI Search, or Databricks Vector Search.
Strong Python skills and hands-on integration with LLM APIs.
System design fundamentals including scalable, reliable, maintainable services, API and integration-boundary design, and trade-offs across latency, throughput, and cost.
Ability to build or extend tool and data connectors for LLM applications.
Experience deploying and operating applications on a managed cloud platform.
Knowledge of AI governance, model lifecycle practices, and evaluation methodology.
Stakeholder and discovery skills to scope ambiguity, work with non-technical business owners, and operate autonomously.

Technologies you’ll work with

Agent & LLM frameworks: LangGraph, Microsoft Agent Framework, LangChain, LlamaIndex
LLM platforms & APIs: Claude API, Azure OpenAI, OpenAI API, model routing and evaluation frameworks
AI coding assistants: Claude Code, OpenAI Codex, GitHub Copilot, Microsoft Copilot Studio
Retrieval & vector search: Azure AI Search, Databricks Vector Search, pgvector
Data & analytics: Databricks, Power BI, SQL, Oracle DB, PostgreSQL
Connectors & integration: MCP (Model Context Protocol), REST APIs, enterprise system connectors, Teams integration
Cloud & deployment: Azure, OpenShift, Docker, Kubernetes, Helm
CI/CD & source control: GitHub, GitHub Actions, Git pull-request workflows
Observability & evaluation: Tracing, evaluation harnesses, LLM observability, logging and monitoring
ITSM & Agile tools: ServiceNow, Jira
Scripting: Python, PowerShell

Preferred qualifications

Solution and system architecture across multiple applications, including security-by-design and reference architecture.
Experience with large-scale data platforms such as Databricks for retrieval, feature work, or pipeline development.
Experience in regulated industries (energy, finance, healthcare) or audit-driven environments.
Background in multi-agent orchestration and context engineering.

Education, location, and compensation

Minimum experience: 5 years
Education: Bachelor’s degree in Computer Science, Data Science, Information Systems, Engineering, or a related field (or equivalent knowledge gained through a combination of education and experience)
Certification (preferred): Cloud or AI/ML certification such as Azure AI Engineer, AWS Machine Learning, or Databricks
Location: Taylor, TX (hybrid, 2 days per week)
Salary: USD 145,000 - 200,000 per year

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2mo ago

Machine Learning Engineer

Samsung Taylor, TX

Taylor, TX

2mo ago Closed

$90k - $175k/yr Full time Anomaly Detection Big Data Data Pipeline

This position is no longer accepting applications (closed Sep 24, 2026).

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.

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Data Jobs in Taylor, TX

Senior Machine Learning Engineer, Defect Quality & Systems

Applied AI Engineer

Machine Learning Engineer

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