Machine Learning Jobs in Austin, TX

Positions 33,486 Updated daily

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.

Principal Software Engineer

Company: ShyftLabs

Location: Austin, TX

Posted Feb 03, 2025

ShyftLabs is seeking an experienced Principal Software Engineer to design and implement scalable high-performance software systems. The role involves collaborating with cross-functional teams, leading technical design and implementation, mentoring a team, and driving innovation. Basic qualifications include a degree in Computer Science, 5+ years of experience in software development, proficiency in Java, Python, and SQL, and experience leading software development teams. The company offers a competitive salary, healthcare insurance, and benefits, with the role being fully remote within the United States.

Product Owner

Company: ShyftLabs

Location: Austin, TX

Posted Feb 03, 2025

ShyftLabs, a growing data product company founded in 2020, is seeking an experienced Product Owner. The role involves defining product vision, leading AgileScrum sessions, and collaborating with crossfunctional teams. The ideal candidate should have 4+ years of experience in a tech company, strong AgileScrum knowledge, and experience with modern tech stacks. The company offers a competitive salary, healthcare benefits, and remote work options, with a preference for candidates near Austin, Texas.

SMB Regional Account Manager (70008032)

Company: Optimum

Location: Dallas-Fort Worth, TX

Posted Feb 03, 2025

Optimum, a leader in connectivity, is seeking enthusiastic professionals to join their team as Small to Medium Regional Account Managers. The role involves forging powerful connections, offering best-in-class connectivity solutions, and delivering an unparalleled customer experience. The successful candidate will be responsible for customer retention, revenue growth, mobile sales prospect and lead generation, and providing exceptional post-sales support. The ideal candidate should have a minimum of 5-8 years of field sales experience, effective communication and problem-solving skills, and proficiency in computer and technical skills. Optimum values a salescentric mindset, empathy, strong interpersonal skills, and extensive product knowledge. The company is committed to empowering employees, upholding transparency, creating community, and demonstrating expertise.

Automotive Technician

Company: CarMax

Location: Fort Worth, TX

Posted Feb 03, 2025

(USA) Merchandising Lead

Company: Walmart

Location: Midland, TX

Posted Feb 03, 2025

Major Account Manager

Company: Palo Alto Networks

Location: Dallas-Fort Worth, TX

Posted Feb 03, 2025

Palo Alto Networks is a cybersecurity company committed to protecting the digital way of life. They value innovation, collaboration, and disruption. The Major Account Manager role involves driving complex sales cycles, understanding customer needs, and positioning Palo Alto Networks' solutions. The company offers a supportive environment with flexible benefits and a focus on teamwork. The ideal candidate has experience in SaaS-based architectures, value selling, and consultative sales techniques.

Senior End-User Compute Administrator

Company: Optimum

Location: Dallas-Fort Worth, TX

Posted Feb 03, 2025

Optimum, a leader in connectivity, is seeking a Senior Enduser Compute and Administration Specialist. The role involves managing and supporting the organization's enduser computing environment, including SCCM and Intune administration. The specialist will ensure system stability, integrity, and efficient operation, providing direct support to endusers and managing system updates. They will also maintain security and compliance of the enduser computing environment. The ideal candidate should have a Bachelor's degree in Computer Science or a related field, 5+ years of experience, and proven experience with SCCM and Intune administration. Familiarity with VMware Horizon VDI workstation support and JAMF is a plus. Optimum values Taking Ownership, Upholding Transparency, Creating Community, and Demonstrating Expertise, and is an Equal Opportunity Employer.

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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