Top Hybrid Machine Learning Engineer Jobs in Los Angeles, CA

Lead design and delivery of advanced data and ML solutions, performing exploratory analysis, statistical modeling, and visualization to inform strategy. Manage and coach teams, collaborate with clients to validate outcomes, and improve analytics processes and data quality to drive business decisions.

Design and productionize causal machine learning models, including uplift modeling and treatment-effect estimation. Analyze A/B tests and quasi-experiments, develop experimentation strategies, evaluate modeling tradeoffs, and build scalable infrastructure. Collaborate with product and engineering teams, conduct code reviews, maintain engineering standards, communicate technical insights, and mentor others.

Lead design and productionalize ML algorithms and pipelines to detect account-sharing fraud in real time. Build scalable preprocessing, training, deployment, monitoring, and A/B testing solutions with cross-functional teams to optimize detection while minimizing user disruption.

Design, build, and deploy production-scale ML models and ranking systems for personalization and advertising. Take end-to-end ownership of models and services, implement evaluation harnesses and automated tests, and maintain data pipelines using Hadoop/Spark/Kafka. Collaborate across teams and present technical findings, while ensuring scalable, reliable system architecture and rapid iteration using AI-assisted coding tools.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

Lead design and delivery of production-ready AI systems within the ION Factory OS, ship AI-driven and core product features, architect scalable cloud-native systems and ML pipelines, implement RAG and MCP integrations, collaborate with product and data teams, ensure reliability and scalability, and mentor junior engineers.

Own end-to-end clinical prediction models: design, train, evaluate, and ship models using physiological time series and EHR data. Define labels with clinical teams, build evaluation/backtesting and regression infrastructure, assess robustness and subgroup performance, support production deployment and monitoring, and contribute to validation and regulatory evidence.

Join Capsule as an AI Engineer to develop ML models for video editing. Responsibilities include researching ML techniques, collaborating on feature development, and optimizing model performance.

Design, evaluate, and productionize decisioning systems that affect pricing, offers, and routing. Run experiments, build models/heuristics and optimization logic under operational constraints, create feedback loops linking model behavior to business outcomes, and partner with engineers to ship improvements. Use AI as an active tool and help the team adopt AI-native workflows.