Top Machine Learning Jobs

As a Senior AI/ML Engineer, you will manage the AI/ML lifecycle, develop predictive models, conduct anomaly detection, and integrate AI capabilities into the Confido platform.

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.

The Senior Lead Machine Learning Engineer will design, implement, and scale ML applications, ensuring performance and high availability while collaborating with cross-functional teams.

Lead design, build, deploy, and maintain production ML models and pipelines at scale. Collaborate with product and data science teams to architect cloud-based ML systems, automate CI/CD, monitor and retrain models, ensure model governance and explainability, and drive high availability and performance.

Develop and implement state-of-the-art machine learning models and statistical methods using massive structured and alternative datasets to discover predictive signals, design experiments, validate ideas through simulation, and collaborate to move research into production trading strategies.

Develop, deploy, and maintain scalable AI/ML solutions for payments including fraud detection and customer experience. Build large-scale data pipelines and feature stores on AWS, fine-tune LLMs/transformers, run experiments, monitor model performance, ensure regulatory compliance, and translate model outcomes into business metrics while working cross-functionally.

Lead end-to-end delivery of production AI/ML capabilities for payments (fraud, anomaly detection, automation, routing). Design and deploy neural/Transformer models, optimize for latency/scale/cost, define evaluation and monitoring, ensure model governance and audit readiness, collaborate with Product/Engineering/Risk, and mentor teams while communicating trade-offs to senior stakeholders.

Lead design, development, and production deployment of document AI solutions (image classification, text categorization, extraction). Architect CV/NLP models (CRNN, transformers), integrate OCR and generative AI, build scalable AWS SageMaker/EKS pipelines, implement MLOps, and lead a team of ML engineers to meet business KPIs.

Design, develop, and deploy scalable AI/ML solutions and agentic LLM systems for payments. Build data pipelines, perform analysis, partner with Product, Technology, Risk and Compliance, track model performance and regulatory documentation, and translate model outcomes into business impact.

Lead end-to-end development and production deployment of causal and predictive ML solutions for private banking. Frame causal problems, build uplift/HTE models and observational causal studies, drive MLOps, monitor model quality, mentor data scientists, and translate causal/LLM research into production in partnership with risk, compliance, and business stakeholders.

Lead development and deployment of AI/ML models for branch network and field workforce optimization using geospatial, graph, reinforcement learning, and behavioral techniques. Manage end-to-end projects, partner with business, governance, UX, and technology, translate model outputs for stakeholders, and ensure regulatory and model governance compliance.

Develop and integrate large-scale data applications using Accumulo, Hadoop, Kafka, Spark, Yarn, Python, and JavaScript. Implement predictive analytics, graph algorithms, and machine learning models; design client/server systems and integration points; improve development workflows and support analytic toolchains for Intelligence Community missions.

Design, implement, and monitor AI/ML models to improve underwriting risk assessment, pricing, and portfolio management. Translate underwriting rules into data models, integrate models into workflows, analyze datasets for risk patterns, build decision-support tools, ensure compliance and fairness, and collaborate with underwriting, actuarial, product, and engineering teams.

The Senior Machine Learning Engineer will develop core ML models, manage data pipelines, and collaborate with stakeholders to deliver scalable ML solutions.

The Software Engineer, Applied AI will develop machine learning solutions and classification models, enhancing text, image, and video understanding on a policy intelligence platform.

The Machine Learning Engineer will lead compliance efforts, review data sets, create databases, perform gap analyses, and evaluate existing AI systems, requiring collaboration with various stakeholders.

Design and optimize machine learning algorithms for sectors like energy and healthcare. Conduct simulations, validate performance, and collaborate with teams.

The Senior Machine Learning Engineer will design innovative machine learning models for drug discovery, leveraging large-scale single-cell datasets and collaborating with ML Scientists to develop optimized models.

As an Applied AI Engineer at Luminai, you will design experiments, optimize machine learning models, and enhance AI platforms for healthcare automation.

The Sr Engineer will develop AI/ML models for health monitoring devices, focusing on algorithm development, data analysis, and cross-functional collaboration.

As a Senior Machine Learning Engineer, you will design and implement systems for ML and NLP, optimizing models and tools for Clover's healthcare solutions.

The role requires a skilled Machine Learning Engineer to create complex models and AI solutions while collaborating with teams to enhance products and services.

Design and implement knowledge distillation pipelines, optimize training and inference performance, and collaborate with research on production-ready ML models.

Lead product strategy for AI-powered personalization and recommendation capabilities. Define requirements, partner with Data Science and Engineering to build, test, launch and optimize models, monitor performance, run experiments, prioritize enhancements using analytics, and communicate results to stakeholders while ensuring scalability, compliance and strong customer experiences.

The role involves building ML systems, improving models across product surfaces, and leveraging data for personalization and user engagement.