Top Hybrid Machine Learning Engineer Jobs

As a Machine Learning Engineer at FanDuel, you'll develop scalable ML applications, enhance user experience through intelligent systems, and collaborate across teams to validate AI insights.

Lead high-impact modeling initiatives and build forecasting systems for financial metrics, applying advanced statistical and ML techniques.

Lead the strategy and development of state-of-the-art end-to-end autonomous driving research, optimizing ML models and collaborating across teams to influence technical decisions.

Build and deploy predictive models and recommendation systems, analyze large-scale user engagement data, design dbt data foundations feeding analytics and experimentation tools, define trusted metrics, and partner with engineering and product teams to drive personalized discovery for millions of members.

The Machine Learning Engineer will develop and test GenAI architectures, fine-tune LLMs, apply ML algorithms, and contribute to architectural design in a collaborative environment.

Lead the development of forecasting models and systems to drive informed decision-making in OpenAI's Strategic Finance team, collaborating with various cross-functional teams.

Design, iterate, and productionize ML models for recommendation, ranking, routing, and monetization. Lead A/B testing and causal measurement, build dashboards and analytics, define metrics, collaborate with Product and Engineering, and scale ML/analytics infrastructure and model lifecycle management.

Design and deploy real-time marketplace algorithms and decision systems combining ML, optimization, and causal inference. Lead end-to-end projects from research to production, mentor scientists, collaborate cross-functionally, and measure impact via experiments and feedback-aware systems.

The Senior Machine Learning Engineer will design, build, and deploy ML solutions, focusing on payments while collaborating with diverse teams to enhance financial applications.

Lead the technical strategy for machine learning modeling, manage production models, mentor ML experts, and enhance ML infrastructure.