Top Machine Learning Jobs

As a Lead Machine Learning Engineer at Capital One, you'll design and implement ML applications, collaborate with teams, and maintain production models, leveraging cloud technologies and best practices.

Lead product efforts for machine learning-driven marketing and acquisition products. Collaborate with data scientists, engineers, and designers to define acceptance criteria, break down epics into MVP features and user stories, prioritize roadmaps, manage agile delivery, and drive stakeholder alignment to build scalable ML product platforms.

The Lead MLE will design, build, and deploy ML applications, collaborating with cross-functional teams and ensuring high performance and governance of models.

Design, build, and productionize core AI systems for document understanding, forecasting, and agentic workflows. Handle messy data ingestion, evaluation, monitoring, anomaly detection, entity reconciliation, and backtesting to deliver reliable, product-focused ML solutions.

Lead ML engineer responsible for architecting, building, deploying, and operating production-scale machine learning systems. Collaborate with product and data science teams to design models, data pipelines, CI/CD, and cloud architectures; retrain and monitor models; ensure performance, reliability, governance, and explainability; and mentor or lead engineering teams.

Lead AI/ML engineering to convert proofs-of-concept into production-grade, enterprise AI/SaaS systems. Drive LLM optimization, RAG and agentic workflows, cloud deployments, model monitoring, ethical and compliant AI, stakeholder engagement, and talent development while embedding with clients and scaling solutions for reliability, performance, and cost-effectiveness.

Design, build, and support high-throughput, low-latency LLM inference and data pipelines; collaborate with data scientists to post-train and evaluate models; implement scalable, production-ready systems with strong testing, monitoring, and performance focus; mentor engineers and iterate on architecture, reliability, and user-facing AI applications.

Conduct ML and deep learning research to build, test, and deploy predictive models for trading. Collaborate with traders, researchers, and engineers to engineer features, preprocess data, prototype algorithms, and improve production research tooling to enhance trading performance.

Lead development and deployment of ML models for trading, drive research and prototyping, oversee data and feature engineering, mentor researchers, and build scalable ML research infrastructure.

Founding ML engineering hire to design and build production ML systems: training and inference pipelines, serving, feature workflows, monitoring, retraining, governance, and operational practices. Partner across engineering, data, product, and applied science to productionize models, implement observability, prevent degradation, and set platform standards and hiring bar for future ML engineers.

Conduct research and develop machine learning models for finance, focusing on deep learning and large language models, optimizing them for performance, and collaborating with teams to integrate ML solutions.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

As a Senior Machine Learning Engineer, you will lead model validation for AI systems, challenge model soundness, and build validation tools for high-stakes areas such as credit and fraud prevention.

Lead full-stack engineering efforts to design, build, and operate cloud-native Java/JavaScript/Python applications and scalable data pipelines. Develop and deploy AI/ML models (cloud and edge), integrate streaming systems (Kafka), manage AWS infrastructure, drive AI-assisted development practices, and provide technical leadership across architecture, code quality, and operational excellence.

Lead end-to-end applied AI/ML solutions for payments trust & safety, from problem framing and data strategy through production deployment, monitoring, and iteration. Drive technical direction, evaluation metrics, reproducibility, and compliance-ready documentation while partnering across product, engineering, data, risk, and compliance stakeholders.

Lead applied ML scientists and managers to build production-grade models and optimization systems for marketing (bidding, capital allocation, ROAS). Own roadmap, OKRs, hiring, cross-functional partnerships, MLOps, experimentation, and production delivery at scale while driving measurable business impact.

Drive technical strategy for the Personalization Platform, develop recommendation systems, maintain ML workflows, and innovate AI solutions for customer engagement.

The Lead Machine Learning Engineer designs and implements ML applications, builds data pipelines, collaborates with teams, and ensures performance and governance of ML systems.

The Sr. Machine Learning Engineer will develop and deploy ML solutions for healthcare, manage data pipelines, and work with large datasets to enhance healthcare delivery.

As a Staff Applied ML Scientist, you will develop and maintain fraud detection models, conduct research on fraud types, and collaborate across teams to drive product development and real-time decision-making.

As a Senior Applied ML Scientist, you'll develop fraud detection models, research new fraud types, and write production code while collaborating across teams to enhance SentiLink's identity verification solutions.

Lead design, architecture, and deployment of end-to-end ML solutions for onboard autonomous driving. Apply foundation-model alignment, unsupervised pre-training, imitation and reinforcement learning, and scale large-model training pipelines. Collaborate cross-functionally, mentor engineers, conduct research, and productionize foundation models for safe, reliable real-world driving.

Design, build, and scale production ML systems for distribution and supply; own end-to-end ML feature delivery from data and modeling to deployment and monitoring; collaborate with cross-functional teams; improve observability, reliability, and operational excellence; mentor engineers and apply advanced ML tooling and platform practices.

Design, build, and operate scalable ML systems and services end-to-end: data sourcing, feature engineering, model development, deployment, monitoring, and operational support. Collaborate across product, data, and engineering, drive ML best practices, and mentor other engineers to improve model reliability, performance, and observability.

Design and deploy end-to-end ML models for ranking, recommendations, search, and personalization. Build scalable data pipelines, monitoring, and evaluation frameworks. Collaborate with product, analytics, and engineering to translate business problems into ML solutions and measure impact via A/B tests and online/offline evaluations.