Top Hybrid Machine Learning Engineer Jobs

Lead research and applied science for personalization and recommender systems across Crunchyroll products. Define problems, build and evaluate models (ranking, retrieval, sequence, bandits, LLM-enabled approaches), design offline/online experiments, collaborate with product and engineering to productionize solutions, and establish best practices for experimentation, reproducibility, and model governance to improve discovery, engagement, and long-term user value.

Lead marketing measurement and experimentation to quantify attribution and incrementality, optimize ad platforms and CRM, build performance data assets, analyze lifecycle engagement, and communicate insights to influence marketing strategy and spend allocation.

As an Applied Scientist, you'll develop and deploy ML models, enhance pricing systems, and work with structured and unstructured data to improve decision-making.

Develop advanced algorithms, apply machine learning, collaborate with clients, and assess new datasets and technologies for data science projects.

Individual contributor on an applied science team building production ML and software systems for media accessibility. Responsibilities include prototyping, evaluation, productionization, experiment design and analysis, code and design reviews, and reporting results for audio/video/NLP/generative AI projects.

The role involves developing AI and ML solutions, collaborating with teams to enhance client experiences, and ensuring proper model lifecycle management.

The Machine Learning Engineer develops production ML algorithms and recommendation systems, collaborates with teams, and optimizes user experiences.

Build scalable, reliable ML and GenAI models and backends; partner with product, engineering, and business stakeholders; improve data collection and quality with data engineers; train and deploy models at scale; publish model insights; analyze trends with statistical significance; present findings to varied audiences; and hire, mentor, and grow the Austin data science team.

Lead design and delivery of large-scale AI/ML and retrieval systems for cybersecurity. Build end-to-end ML architecture, models (anomaly detection, ranking, RAG/LLM), and production infrastructure. Drive technical strategy, cross-functional initiatives, experimentation, and mentor junior scientists while balancing latency, scalability, reliability, and business impact.

Work on AI-powered Notion features: prototype and ship model-powered product improvements, integrate and productionize models, improve model performance and reliability, and build evaluation frameworks and experiments to measure and improve quality.