3350 Machine Operator Engineering jobs in Atlanta,GA

Applicants must currently be authorized to work in the United States for any employer without current or future sponsorship. No OPT, CPT, STEM/OPT or visa sponsorship now or in future.
Bachelor's degree in a related discipline and 6 years' experience in Machine Learning; or a different combination, such as a master's degree and 4 years' experience; a Ph.D. and 1 years' experience in a related field; or 18 years' experience in a related field with no degree
Minimum of 6 years of experience as a Machine Learning Engineer or equivalent
Deep expertise in multiple ML domains and familiarity with emerging research areas
Strong experience in technology evaluation, competitive analysis, and strategic planning
Comfortability with non-deterministic systems
Product background- understand how to prioritize, collaborate across teams, manage dependencies with others, set strategy
Experience in Rally, Jira or similar tools
Skilled in analytical thinking, consulting, requirements analysis, system and technology integration and technology savvy.
Skilled in collaborating with intent, communicating with impact, developing trust, driving innovation and striving for excellence.
Proven track record of leading innovative projects from concept to proof-of-concept
Demonstrated success in knowledge sharing and thought leadership (publications, speaking, etc.)
Experience building and leading high-performing research or innovation teams
Excellent communication skills for technical and executive audiences
Strong network within the ML research community
Experience with research collaboration and partnership development
Proficiency in AI development tools (GitHub Copilot, Claude, GPT-4) for ML development with ability to validate AI outputs for production readiness.
Understanding of agentic frameworks (AWS AgentSquad, AWS Strands, LangChain, agent patterns) with progression from basic configuration to custom enterprise system design.
Knowledge of AI ethics, responsible AI practices, and governance frameworks for business-critical ML deployment.
Ability to leverage AI like Co-Pilot for technical communication to stakeholders and cross-functional collaboration.
Commitment to continuous learning in AI-augmented data science and responsible AI use.