Top Hybrid Machine Learning Engineer Jobs

Lead ML work for send-time, channel, and propensity optimization to increase engagement. Design and ship recommendation/sequential-decision systems, ensure experimental rigor, own production models end-to-end, and mentor a small ML team while partnering with product, engineering, and growth.

The Machine Learning Scientist will build and integrate ML models, analyze data, design scalable pipelines, and ensure robust model testing.

Build end-to-end internal AI-powered tools and systems using agentic coding workflows. Own full-stack development: backend services, pipelines, frontends, CI/CD, cloud deployments, MCP integrations, and dashboards to enable AI-assisted engineering across the company.

Design, implement, train, validate, and deploy machine learning models at enterprise scale. Collaborate with data engineers and software engineers to preprocess data, perform feature engineering, evaluate model performance, and integrate models into production. Continuously improve models and adopt new ML and cloud data tools to solve business problems.

Lead the data and ML product vision: define roadmap, prototype models on noisy behavioral datasets, translate signals into user-facing features, partner with engineering to productionize ML, and generate insights to inform product roadmaps.

The role involves developing ML-based planning algorithms for autonomous trucks, ensuring safety checks, real-time operation, and keeping up with research. Requires strong communication and collaboration.

Develop, validate, and improve machine learning models (regression, classification, forecasting, optimization). Prepare data, perform feature engineering, train and evaluate models, and support deployment with Data/ML Engineering. Communicate results, document methods, and collaborate on experiments and cross-functional initiatives.

The ML Data Engineer develops and maintains machine learning pipelines and systems, collaborating with teams to optimize manufacturing processes through data-driven insights and predictive models.

Lead a small ML engineering pod to turn research into production: design and operationalize training pipelines and low-latency inference services for computer vision, document intelligence, and predictive models. Drive R&D execution, benchmarking, prototyping, migrations from frontier models to fine-tuned/open-source solutions, and mentor early-career engineers while aligning research with product timelines.

Design, scope, build, and deploy AI-powered GTM workflows and internal tools that reduce friction and boost revenue-team productivity. Own projects end-to-end from business case to production, partner with Ops/Analytics/Product/IT, and drive adoption and measurable impact.