Machine Learning Jobs in Austin, TX

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Looking for Machine Learning jobs in Austin, TX? Browse our curated listings with transparent salary information to find the perfect Machine Learning position in the Austin, TX area.

Form Carpenter

Company: Turner Construction Company

Location: San Antonio, TX

Posted Feb 14, 2025

Proficient knowledge of materials, methods, and tools involved in construction or repair of buildings or other structures, foundations, and framing.

Customer Service/Inside Sales

Company: Allstate Insurance- Local agent in Austin TX

Location: Austin, TX

Posted Feb 10, 2025

Must have Property & Casualty license. The Service Professional opportunity is not an employment opportunity directly with Allstate Insurance Company, but…

AI Agent Engineer

Company: Naptha AI

Location: Austin, TX

Posted Feb 13, 2025

Build and deploy AI agents using various frameworks. Assist with agent integration and testing. Basic understanding of AI concepts.

Mid-Senior Level Federal Financials ERP Business Analyst

Company: CGI Group, Inc.

Location: Austin, TX

Posted Feb 13, 2025

5+ years of experience as a Business Analyst supporting a system implementation (requirements analysis, process design, validation, testing, implementation…

Sanctions FIU Investigations Lead

Company: Meta

Location: Austin, TX

Posted Feb 08, 2025

Electrical General Foreman

Company: DP Electric

Location: Pflugerville, TX

Posted Feb 15, 2025

Journeyman Electrician license or equivalent certification. The Electrical General Foreman plays a pivotal role in driving the successful execution of…

Pharmacist - PRN

Company: TELYRX DALLAS LLC

Location: Dallas, TX

Posted Feb 14, 2025

An actively licensed pharmacist in the state of Texas with no reprimands on their license. Respond to clinical questions online or calls from patients.

Team Member Trainer

Company: Target

Location: Denton, TX

Posted Feb 08, 2025

Project Manager - Land Development & Infrastructure Design

Company: ESP Associates

Location: San Antonio, TX

Posted Feb 15, 2025

ESP Associates has an immediate opportunity in our San Antonio office for a Project Manager with experience in civil engineering land development,…

Frequently Asked Questions

What are typical salary ranges by seniority for machine learning roles?
Entry‑level ML Engineer: $90k–$120k; Mid‑level ML Engineer or Data Scientist: $120k–$160k; Senior ML Engineer or Research Scientist: $160k–$220k; Lead ML Engineer or Principal Research Scientist: $200k–$280k; AI Product Manager: $130k–$180k depending on experience and market.
What skills and certifications are most valuable in machine learning today?
Core language: Python; Deep learning frameworks: TensorFlow, PyTorch; Scikit‑learn for classical models; SQL and NoSQL databases for data ingestion; Docker and Kubernetes for deployment; Cloud AI services such as AWS SageMaker, GCP Vertex AI, Azure ML. Certifications: TensorFlow Developer Certificate, AWS Certified Machine Learning – Specialty, GCP Professional Machine Learning Engineer.
How common is remote work for machine learning positions?
Over 70% of ML roles allow full remote or hybrid arrangements. Startups and fintech firms tend to offer 100% remote options, while larger enterprises often provide hybrid models with occasional on‑site data‑center visits. Remote work is especially prevalent for roles focused on model training and research.
What career progression paths exist in machine learning?
Typical paths: ML Engineer → Senior ML Engineer → Lead ML Engineer → ML Manager; Data Scientist → Senior Data Scientist → Lead Data Scientist → Head of Data; Research Scientist → Senior Research Scientist → Principal Scientist → Chief Data Scientist; ML Ops Engineer → Senior ML Ops Engineer → Lead ML Ops Engineer → Director of MLOps. Progression often involves moving from coding to architecture, then to leadership and strategy.
What are the current industry trends shaping machine learning hiring?
Key trends: reinforcement learning for autonomous systems; federated learning for privacy‑preserving models; edge AI for IoT devices; AutoML platforms speeding model deployment; MLOps practices for scalable pipelines; explainable AI and ethics compliance; and increased demand for AI governance roles.

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