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.

Lead Machine Learning Ops Engineer

Company:

Location: Houston, TX

Posted Jan 30, 2025

Lead Machine Learning Ops Engineer

Company:

Location: Houston, TX

Posted Jan 30, 2025

Plan Review Consultant

Company:

Location: Frisco, TX

Posted Jan 30, 2025

Lead Machine Learning Ops Engineer

Company:

Location: Houston, TX

Posted Jan 30, 2025

Patient Care Technician

Company: DaVita

Location: Baytown, TX

Posted Jan 30, 2025

Senior, Software Engineer | Web Engineer | Javascript, React & Node.js

Company: Walmart Global Tech

Location: Dallas-Fort Worth, TX

Posted Jan 29, 2025

Walmart eCommerce is seeking a skilled Web Engineer with a strong background in JavaScript, React, and Node.js to join their Web Platform team. The successful candidate will be responsible for designing and delivering high-quality, efficient, and scalable code, collaborating with backend teams, and participating in code reviews. They will also be involved in mentoring fellow engineers, implementing new features, and conducting oncall rotations. The role requires a Bachelor's degree in Computer Science or related field and 5+ years of experience in software engineering. Walmart offers competitive compensation, benefits, and opportunities for professional growth.

Consulting, Manager

Company: Deloitte

Location: Dallas, TX

Posted Jan 30, 2025

Lead Machine Learning Ops Engineer

Company:

Location: Houston, TX

Posted Jan 30, 2025

Senior Engineer, DevOps

Company: DISCO

Location: Austin, TX

Posted Jan 30, 2025

DISCO is seeking a Senior DevOps Engineer with extensive experience in AWS, cloud-native applications, and Infrastructure as Code. The role involves delivering high-quality systems, architecting scalable solutions, optimizing performance, providing technical mentorship, and collaborating cross-functionally. The ideal candidate will have experience with PaaS platforms, Windows Servers, other cloud platforms, Jenkins, and software development. DISCO offers a competitive salary, RSUs, flexible PTO, and growth opportunities in a company revolutionizing the legal industry.

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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