Top Hybrid Machine Learning Engineer Jobs

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.

Partner with enterprise customers to identify AI use cases, architect and implement custom applications and integrations using WRITER's platform, APIs, Knowledge Graphs and LLMs, drive adoption, provide product feedback, and reduce time-to-value through scalable processes and documentation.

Lead the fraud ML strategy and end-to-end development of real-time fraud detection models. Mentor data scientists, design credit/lending fraud models, perform unsupervised and network analyses, run A/B experiments, deploy and monitor models in cloud, and build dashboards to report KPIs and support cross-functional stakeholders.

Build, deploy, and maintain risk and eligibility models (credit, accident, fraud, behavioral). Manage modeling platform and vendor integrations, monitor model performance and drift, perform feature selection and validation, and collaborate with Risk, Finance, Product, and Engineering to set thresholds and translate results into actionable recommendations.

Develop, train, fine-tune, deploy, and monitor large-scale ML models (LLMs, speech, TTS, multimodal). Collaborate with cross-functional teams, run extensive experiments and evaluations, influence product strategy, and mentor engineers to deliver scalable, high-quality AI solutions.

Manage end-to-end data analytics engagements: gather requirements, plan projects, coordinate cross-functional teams, build data pipelines, apply statistical and machine learning methods, create visualizations and dashboards, ensure data quality, support CI/CD deployments, and present insights to clients.

Embed with enterprise clients to scope, build, and deploy production LLM-powered systems (agents, retrieval, evals). Lead technical design, ship full-stack solutions, ensure observability and live operations, and sustain client relationships through deployment and operation.