Top Senior Machine Learning Jobs

Design, develop, train, evaluate, and deploy machine learning models using large datasets. Integrate ML/AI solutions into production systems, perform data preprocessing, model selection, cross-validation, and optimize model performance while supporting system architecture and engineering tasks.

Lead and scale the Ads Data team, owning data and ML strategy across retrieval/ranking, bidding/auction systems, and data engineering. Hire and mentor applied scientists and analytics/engineers, set technical roadmap and operating model, partner cross-functionally, contribute hands-on to high-priority ML problems, and represent Ads Data in company strategy.

Lead research, development, and deployment of recommendation and personalization ML models. Design algorithms, build end-to-end pipelines, optimize for accuracy/latency/scale, integrate solutions into production, implement monitoring/bias detection/retraining, and collaborate on experimentation and product integration to improve user engagement and retention.

Design, develop, validate, and deploy scalable AI/ML and simulation/optimization solutions using structured and unstructured data. Lead model development within model risk frameworks, mentor junior scientists, collaborate with engineering and product teams for MLOps/cloud deployments, and communicate actionable insights to business partners while ensuring regulatory and risk compliance.

Architect role to design full-stack, large-scale solutions informed by leading tech companies. Requires deep ML/AI and large-data analysis knowledge, proficiency across languages/frameworks (Go, Scala, Python, R, MATLAB, Node.js, React), NoSQL expertise, and familiarity with mobile testing, deployment pipelines, and SRE practices. US work authorization required.

Lead development and administration of Quality digital tools, build UIs, dashboards, and automated data pipelines. Perform data analysis, predictive/time-series modeling and apply ML/AI to improve warranty, claims validation, and customer experience. Serve as SME for warranty/data platforms, deliver analytics, run PoCs, and partner with stakeholders to reduce warranty cost and lead-times.

Lead machine learning initiatives, oversee product development, improve execution pace, enhance team reliability, and foster team growth at Hightouch.

Lead the development of pricing algorithms for vehicle valuation at CarMax, managing a team of data scientists to implement strategies and maintain optimal pricing models.

You will lead and manage a team of engineers developing machine learning solutions for perception product challenges in autonomous driving.

The Senior Machine Learning Engineer leads technical direction for ML systems focused on personalization and ranking, overseeing architecture and implementation while mentoring team members.

Drive end-to-end ML solutions for Uber Freight: identify opportunities, research techniques, prototype, deploy and monitor production models at scale, and integrate Generative AI/agentic systems to optimize logistics and operations.

Lead development of interpretable, production-quality valuation models for residential real estate. Partner with the CTO to design methodologies, quantify uncertainty, evaluate model performance, and translate research into scalable, maintainable ML systems. Mentor hires, set technical standards, and collaborate with engineering to deploy trusted analytics used across the country.

Lead development and deployment of AI and analytic solutions for healthcare using advanced statistical and machine learning methods. Provide enterprise consultative services, design experiments, build predictive/prescriptive models, direct analysis and reporting, and lead technical teams to translate insights into clinical applications.

Act as the technical lead in enterprise sales: run technical discovery, design and deliver customized demos and POCs, assess API/cloud/model fit, address technical objections, support contract negotiations, and relay product feedback to engineering while staying current on AI/ML trends.

Provide advanced discovery and analytical support using machine learning, statistical analysis, scripting, automation, and TechSIGINT tools. Develop analytical prototypes, create data visualizations, document methods and collaborate with analysts and engineers to support mission-focused discovery efforts and adopt new analytical technologies.

Design, build, and deploy production-grade AI/ML models, agentic workflows, and data pipelines using Python, Databricks, LangChain/LangGraph, and Azure. Collaborate with cross-functional teams to translate requirements into scalable LLM applications, implement feature engineering and validation frameworks, and embed responsible AI practices including fairness, security, and monitoring.

The Machine Learning Engineer will design, build, and deploy ML applications at scale, collaborating with cross-functional teams and applying best practices in ML engineering and cloud technologies.

Lead a team of Machine Learning Engineers in designing, building, and implementing ML applications, focusing on data-intensive solutions and infrastructure decisions, while promoting best practices in AI.

Design, build, and productionize large-scale foundation models and ML systems (transformer architectures, self-supervised learning). Architect and implement training, data pipelines, deployment, monitoring, and CI/CD. Collaborate with cross-functional teams, ensure model governance, reliability, and performance, and apply ML engineering best practices at scale.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

As a Machine Learning Engineer in Fraud Risk, you'll build and maintain ML systems for fraud detection and anomaly analysis, ensuring robust performance and real-time decision making.

Design, build, and own a multi-tenant enterprise SaaS backend that feeds ML data and BI. Collaborate with product, data science, UX, and users to deliver scalable, reliable customer-facing AI solutions. Drive systems design, database schemas, development lifecycle standards, technical docs, and code reviews while using AI/agentic tools to accelerate development.

Lead analytics and ML work to drive Aetna Medicare growth: design experiments, segmentation, forecasting, and end-to-end ML system architecture; prototype models, collaborate with engineering and product teams for production deployment, and communicate data-driven recommendations to cross-functional stakeholders.

Lead development of large-scale, low-latency ML systems for ad serving to optimize pricing, allocation, and auctions. Design experiments, build production ML pipelines and model serving, partner cross-functionally, and provide technical leadership and mentorship.

Design, prototype, and deploy AI-powered solutions by partnering with business stakeholders and engineering teams. Analyze processes, identify AI automation opportunities, build POCs/MVPs using LLMs and agents, define KPIs, document and train users, and drive adoption and scalability of reference AI solutions across the organization.