Top Hybrid Machine Learning Engineer Jobs

Develop ML and multimodal AI systems that integrate neurophysiological, behavioral, and sensor data for cognitive state estimation and decision support. Build algorithms and prototypes, apply cognitive science to improve explainability and human-AI collaboration, contribute to proposals/publications, and demonstrate capabilities for mission-driven R&D.

The Staff Machine Learning Engineer will design and build scalable ML systems, mentor engineers, and drive AI strategy at SailPoint, focusing on AI-powered identity security solutions.

Lead development and deployment of predictive and prescriptive models focused on supply chain and causal modeling. Partner cross-functionally to translate business problems into data science solutions, mentor team members, explore large datasets, communicate results to senior stakeholders, and contribute to the ML/AI strategy.

Build and maintain internal agentic AI platforms and developer-facing tools that integrate LLMs with engineering workflows. Partner with product and infra teams to design standards for agent deployment, reliability, evals, security, onboarding, and adoption measurement to improve developer productivity.

Lead product strategy and delivery for Kai, Airwallex's AI assistant. Partner with engineering, design, and cross-functional teams to build scalable LLM-powered features, validate new agentic use cases, balance model quality and cost, and drive product performance using user feedback and production conversation data.

Design, build, and productionize causal ML models (uplift, heterogeneous treatment effects), analyze A/B tests and quasi-experiments, collaborate with product and engineering, ensure scalable, maintainable infrastructure, and balance model complexity with interpretability and business impact.

Lead the simulation product vision and roadmap, define simulation environments for perception, autonomy, and navigation, prioritize realistic customer use cases, translate user research and data into requirements, and coordinate cross-functional execution to enable testing and adoption of robot fleet software.

Own an area of the revenue lifecycle to design, build, and operate AI agents and automations for GTM teams. Conduct stakeholder research, embed in field workflows, ship and iterate on agent outputs, monitor adoption and quality, scale patterns, and partner cross-functionally to drive measurable revenue impact.

Lead cross-functional analytical efforts to drive product, commerce, and risk outcomes. Design experiments, causal inference, and statistical models; build and scale fraud/risk interventions using heuristics and machine learning; measure feature impact; and communicate actionable insights to stakeholders and executives.

Build, deploy, and monitor machine learning and pattern-recognition models to detect lending fraud. Analyze large transactional datasets with Python and SQL, partner with Fraud Operations to iterate on features and reduce false positives/negatives, communicate findings to stakeholders, apply AI in production, and mentor team members.