Top Senior Machine Learning Jobs

Lead end-to-end deployments of AI for hotel revenue teams: integrate PMS/CRS/RMS, ship ML/optimization models and agentic workflows, validate recommendations with revenue managers, define metrics, drive adoption, and hire/mentor the forward deployed engineering team.

Design, develop, train, evaluate, deploy, and monitor scalable AI/ML models to predict member outcomes. Perform EDA, feature engineering, data extraction with SQL/Python/R, ensure data quality and fairness, build CI/CD pipelines, containerize models (Docker/Kubernetes), and communicate results to non-technical stakeholders. Support model maintenance, logging/alerting, unit testing, and team best-practice initiatives.

Lead development and deployment of advanced ML and deep learning models, ensure model validation and MLOps best practices, source and validate data, translate business problems into scalable solutions, collaborate cross-functionally, and mentor data science team members.

Embed with Marketplace teams to discover and prioritize AI opportunities, rapidly prototype and validate AI/LLM solutions using enterprise data, and partner with Technology and Data teams to productionize, measure, and drive adoption of scalable automation and ML solutions.

Apply advanced AI, ML, and computational modeling to drug discovery and early development. Build scalable data pipelines and predictive models, integrate diverse scientific datasets, ensure model validation and reproducibility, and communicate insights to multidisciplinary teams to support decision-making.

Build and productionize scalable ML infrastructure for training, evaluating, and serving large language models. Develop distributed training and inference pipelines, optimize LLM latency, create automation abstractions, and collaborate cross-functionally to scale ML systems and best practices.

Design, build, and productionize real-time fraud detection models. Advance adversarial-domain techniques (graph, sequence, anomaly detection), design robust evaluation for biased/delayed labels, ensure low-latency reliable deployment, monitor models in production, and collaborate with data scientists and stakeholders to translate fraud signals into modeling solutions.

Lead global strategy to optimize and modernize financial crime detection across transaction monitoring, customer risk rating, and fraud models. Drive transition from rules-based controls to AI/ML-driven detection, oversee model lifecycle governance, reduce false positives, ensure regulatory defensibility, and partner with Compliance, Technology, Data, Operations, and vendors to deliver measurable risk and operational improvements.

Work with enterprise customers to identify high-value tabular ML use cases, design production-grade architectures, prototype solutions, lead deployments, coordinate cross-functional teams, and codify reusable deployment patterns to scale adoption of the NEXUS tabular foundation model.

Lead design, build, and deployment of scalable ML/AI models (including image/vision) to production. Collaborate with engineering and business teams, monitor model quality and bias, and apply data-driven solutions to predict and aggregate risk.

Lead improvements to behavior and trajectory prediction for autonomous vehicles. Own prediction work within Planning, collaborate with ML, Perception, Simulation, and Systems teams, refine prediction-planning interfaces, define metrics and evaluation frameworks, analyze failures via logs/simulation, and lead technical projects while mentoring junior engineers.

Develop and improve machine learning models and systems for clinical outcomes. Lead ML initiatives, mentor teams, and establish best practices in AI. Collaborate across engineering, product, and design for data-driven solutions.

The Senior Research Engineer will develop and deliver scalable ML solutions, collaborate with teams, and apply innovative technologies in AI to impact professionals in legal and accounting fields.

Lead cross-functional ML/AI initiatives, drive technical roadmaps, mentor teams, and deliver impactful ML/AI solutions for business growth.

The Senior Principal, ML/AI will lead cross-functional teams in developing and implementing ML/AI solutions, driving significant business impact through innovative technical leadership over a defined roadmap.

The role requires developing machine learning models for cyber threat detection, optimizing response times, and utilizing innovative tools for cybersecurity applications.

Design and implement AI/ML solutions for operational planning by building and deploying predictive models, preprocessing and analyzing large datasets, and creating dashboards. Translate operational requirements into data-driven capabilities and collaborate with planners, operators, and technical teams to deliver secure analytics that support DoD mission decision-making.

Design and maintain data models and pipelines for mortgage and lending platforms. Develop ML/AI, predictive and prescriptive analytics, and LLM/Generative AI solutions. Collaborate with engineering and business stakeholders to build scalable cloud and data warehouse solutions, ensure data quality and governance, and deliver dashboards and executive reporting.

Lead design and delivery of end-to-end revenue optimization products using forecasting, ML/reinforcement learning, and optimization. Partner with cross-functional teams to identify opportunities, prototype solutions, implement analytics, and support deployment while developing the analytics team and driving commercial impact.

Lead data analytics and ML efforts to optimize onboard revenue streams. Analyze complex datasets, build predictive models and recommendation systems, maintain data pipelines, and create dashboards. Collaborate cross-functionally with revenue management, operations, marketing, finance, and IT to implement data-driven solutions that improve guest experience and revenue performance.

Lead design, development, validation, and cloud deployment of advanced time-series forecasting and explainable ML models for finance and risk. Build AI agentic systems, run simulation and uncertainty quantification, produce model risk documentation, and collaborate with business, finance, and risk stakeholders to translate needs into dashboards, reports, and compliant models.

Use data analytics and machine learning to optimize onboard revenue and guest experience. Analyze complex datasets, build predictive models and recommendation systems, maintain data quality and automated pipelines, create dashboards and reports, and collaborate across revenue management, operations, marketing, finance, and IT to drive data-driven decisions and revenue growth.

Lead design, development, deployment, and maintenance of large-scale ML systems and pipelines. Provide senior technical consulting, resolve complex ML and governance issues, plan end-to-end model integrations, and document implementations for performance optimization.

The Staff Machine Learning Engineer will lead the development of search ranking systems, focusing on machine learning models for relevance, quality, and personalization through various advanced techniques and collaboration with cross-functional teams.

As a Principal AI/ML Engineer, you will lead the development of machine learning solutions for advertising, focusing on optimization, personalization, and deploying models within a highly scalable infrastructure.