Top Hybrid Machine Learning Engineer Jobs

Lead end-to-end technical engagements to design, build, and deploy agentic AI and LLM-powered applications (RAG, fine-tuning, multi-agent systems). Own full ML lifecycle, backend services, integrations across cloud/on‑prem, and production LLMOps. Coordinate cross-functional teams, mentor junior engineers, and represent H2O.ai in customer workshops and executive briefings to deliver production-grade, compliant AI solutions.

The role involves designing and deploying machine learning systems for content safety at Spotify, collaborating with various stakeholders, and providing technical leadership.

Build production decision systems that estimate causal effects and uncertainty, learn from interventions, optimize constrained policies (pricing, inventory, promotions), evaluate counterfactuals and challengers, and deploy safe, automated ML-driven business decisions to generate measurable economic lift.

The ML Bioengineer will conduct research on next-generation clinical and genome language models, collaborating with experts to develop machine learning methodologies and analyze data while contributing to project milestones and documentation.

Design, implement, and optimize machine learning and deep learning models; preprocess and analyze large datasets; integrate models into production; monitor performance and troubleshoot; collaborate with engineers, data scientists, and stakeholders; stay current with ML/AI advances.

Lead development and production deployment of advanced ML/AI pricing models across Home, Renters, Car, and Pet lines. Own end-to-end projects, align stakeholders, mentor junior data scientists, set technical standards, and drive adoption of advanced data science practices within Insurance.

Embed with strategic customers to own end-to-end delivery of robotics/egocentric data products: translate requirements, sequence and QA deliveries, triage technical issues, and feed recurring needs into the data-engine and annotation roadmaps to improve model performance.

Own end-to-end matching and ranking systems for out-of-home advertising: design candidate generation and re-ranking models, build and manage data warehouse and pipelines, implement evaluation and monitoring, publish low-latency ranking APIs, and maintain model observability and retraining workflows.

As an AI/ML Engineer, you will develop and optimize AI solutions for renewable energy assets, collaborating with a skilled team to deploy algorithms in real-world projects.

Design, build, and deploy machine learning models to detect fraud, prevent losses, and monitor risk. Work with Product, Engineering, and Operations to turn large-scale data into production-ready pipelines and model-serving solutions, monitor performance, and continuously improve model accuracy and business impact.