Top Hybrid Machine Learning Engineer Jobs

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.

Build and deploy production ML models and scalable cloud data pipelines for manufacturing IoT data. Bridge OT/IT systems, apply statistical/time-series methods, run experiments, and translate results to operations and executives to improve OEE, uptime, yield, and quality.

The AI Enterprise Engineer will work with enterprise customers to define model specifications, analyze agent behavior, and benchmark models using Bespoke Labs tools, requiring a strong foundation in AI/ML and production deployment.

Design and build the data and analytics layer: schemas, pipelines, APIs, dashboards, forecasting and ML models. Deliver usage-based metering, ensure data correctness and observability, and ship customer-facing analytics while raising engineering standards.

Paid 12-week hybrid Ph.D. research internship conducting original ML research for world models and autonomous driving. Responsibilities include designing, training, and evaluating models (world modeling, model-based RL, 3D perception), prototyping in closed-loop simulation and hardware, working with large datasets, collaborating with researchers, and publishing results.

Develop and implement machine learning models for evaluating disputes and chargebacks, and create a model orchestration layer. Collaborate on data use for enhanced user experiences.

As a Machine Learning Scientist at Nucs AI, you will develop innovative ML methods for medical image analysis, collaborate with clinicians, and contribute to clinical validation studies, impacting cancer diagnostics.

Develop and productionize ML-driven physics simulation capabilities: experiment with temporal propagation, generate and curate training/verification data, iterate prototypes to production, collaborate with physicists, AI researchers, engineers, and customers to scale transient PDE and dynamics modeling.