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8958 Machine Learning Engineer Aic Core jobs in Los Angeles,CA

New, Posted 9 hours ago

AI/ML Engineer - Python/GenAI/.Net/C# - Remote

UnitedHealth Group

Los Angeles, CA

$72,800-$130,000/yr

RemoteRetirement Benefit

Machine Learning Engineer Intern, Summer 2026

Snap

Los Angeles, CA 90079

Sr Machine Learning Engineer

Disney

Burbank, CA 91505

$155,700-$208,700/yr

~ 21 min OnsiteUrgently Hiring

Build emulation and simulation environments (digital twins) of the infrastructure to test AI/ML-driven automation under realistic scenarios and allow for faster ideation and iteration for architects and engineers.
Applies systems thinking to understand how individual components fit into larger, more holistic solutions.
Demonstrated success designing and building enterprise-scale systems and reusable software frameworks.
Capable of quickly shifting between detailed, hands-on work and high-level strategic thinking.
Bachelor's degree in Computer Science, Information Systems, Software, Electrical or Electronics Engineering, or comparable field of study, and/or equivalent work experience
Experience with AI/ML technologies and data engineering concepts. Preferred: Proven hands-on building AI agents.
7+ years of software engineering experience, with expertise in automation, machine learning, and AI technologies
Solid foundation in statistics, data analysis, and applied machine learning techniques
Proven hands-on experience using software to build frontend, APIs and backend functionality; strong proficiency with Python, JavaScript, TypeScript, Go, or Rust
Experience operationalizing models in distributed, production environments
Design, train, and deploy machine learning models for anomaly detection, forecasting, predictive analytics, event correlation, pattern recognition, classification, causal analysis, and more in distributed environments that can be used to surface leading indicators of failure
Experience working with large-scale, real-world datasets (noisy, incomplete, non-standardized, and evolving)
Ability to translate ambiguous operational problems into solvable machine learning use cases
Experience with modern cloud platforms, container orchestration (Kubernetes/Docker), identity/auth frameworks, data and workflow orchestration.
Strong hands-on experience building and deploying event-driven or streaming data, machine learning models in production
Proven hands-on experience building production-grade ML models and inference pipelines; strong proficiency with modern ML frameworks such as PyTorch, TensorFlow, Scikit-learn, etc.
Strong communication, collaboration and leadership skills