Top Hybrid Machine Learning Engineer Jobs

Design and deploy machine learning models for financial document processing, optimize large language models, collaborate with cross-functional teams, and guide junior peers in a fast-paced environment.

Research, design, and implement discrete optimization and mission-planning algorithms for multi-agent autonomous systems; improve and maintain C++ mission-planning software; define technical roadmaps; mentor engineers; perform full software and hardware-in-the-loop simulations.

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.

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.

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.

The Machine Learning Scientist will develop AI frameworks for predictive modeling in oncology, collaborating with teams to enhance drug R&D efforts through advanced machine learning techniques and multimodal data integration.

Design, develop, and deploy end-to-end machine learning and large language model solutions. Perform data discovery, cleaning, preprocessing, and visualization. Build/train LLMs and Agent+RAG systems, collaborate with stakeholders and AI teams, and apply graph-based analysis to support enterprise integration and analytics.

Develop, evaluate, and deploy machine learning models for real trading environments; apply rigorous statistical techniques to measure performance, control overfitting, and assess model risk and interpretability; collaborate with traders and developers; maintain best practices and production-ready systems.

Founding ML engineering hire to design and build production ML systems: training and inference pipelines, serving, feature workflows, monitoring, retraining, governance, and operational practices. Partner across engineering, data, product, and applied science to productionize models, implement observability, prevent degradation, and set platform standards and hiring bar for future ML engineers.

Build a zero-to-one AI-native go-to-market platform embedded with revenue teams. Define architecture, ship prototypes to production, iterate on LLM-driven agentic orchestration and automation, and measure success by business outcomes while ensuring security and supportability.