Machine Learning Jobs in Atlanta, GA

Positions 15,699 Updated daily

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

Cloud Data Engineer

Company: Deloitte

Location: Atlanta, GA

Posted Jan 30, 2025

Sr Manager, Software Engineering

Company:

Location: Morrow, GA

Posted Jan 30, 2025

Executive Recruiting Administrator

Company: Aprio

Location: Atlanta, GA

Posted Jan 30, 2025

Aprio is a nationally ranked CPA and advisory firm with 22 US office locations and over 2100 team members. They offer a top-rated culture, vast growth opportunities, and a next-generation career move. The firm is seeking an Executive Recruiting Administrator to join their dynamic team. The role involves managing candidate scheduling, preparing interview materials, tracking candidates, and handling administrative tasks. The ideal candidate should have a Bachelor's degree, 5+ years of recruiting experience, strong organizational skills, and proficiency in Microsoft Office Suite and Applicant Tracking Systems. Aprio offers competitive compensation, benefits, and a flexible work environment.

Area Manager, Wet Mill

Company:

Location: Atlanta, IL

Posted Jan 30, 2025

Area Manager, Wet Mill

Company:

Location: Atlanta, IL

Posted Jan 30, 2025

Sr. HR Systems Analyst

Company: Equifax Inc.

Location: Atlanta, GA

Posted Jan 30, 2025

Equifax is seeking a candidate for the HR Systems Team, offering a fun, fast-paced, and collaborative environment. The role involves providing support for Compensation functional areas in Workday, as well as other HR systems. The candidate will participate in global HR data governance, assist with testing activities, and partner with other systems and IT resources. Workday Compensation Configuration experience is required, along with systems support experience, understanding of business software, and strong communication skills. Equifax offers comprehensive benefits and growth opportunities.

Patient Care Technician

Company: DaVita

Location: Loganville, GA

Posted Jan 30, 2025

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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