Top Senior Machine Learning Jobs

Lead design and implementation of complex embedded and application software for signal processing and algorithm development (including machine learning) for EO/IR and electronic warfare systems. Provide technical leadership, mentor teams, produce design/project documentation, evaluate and optimize algorithms (C++), and collaborate with systems, hardware, and test engineers through full lifecycle development and customer technical reviews.

Lead and mentor an agile engineering team to design, build, test, and operate secure, cloud-native production software. Provide technical direction, deliver high-quality code, drive product design decisions, and advocate firmwide engineering practices while applying AI/ML expertise.

Design, train, evaluate, and productionize machine learning models (including embeddings and foundation models) on large-scale data. Partner with product and engineering to run experiments, improve customer experiences, and deploy scalable solutions using Spark and AWS.

Build, deploy, and monitor machine learning and pattern-recognition models to detect lending fraud. Analyze large transactional datasets with Python and SQL, partner with Fraud Operations to iterate on features and reduce false positives/negatives, communicate findings to stakeholders, apply AI in production, and mentor team members.

Build and validate ML and statistical models, perform exploratory and descriptive analyses, create visualizations, maintain data pipelines and quality, collaborate with clients to deliver tailored AI/ML solutions, mentor junior staff, and apply modern AI techniques including generative AI and NLP to solve business problems.

Review and audit machine learning models and model risk practices across the enterprise. Partner with cross-functional teams to design, train, evaluate, validate, and implement ML and NLP solutions, using tools like Python, Spark, H2O, and AWS. Translate technical findings into business recommendations and assurance for stakeholders and the Audit Committee, while researching and applying emerging Generative AI and state-of-the-art methods.

Design, build, and deploy production-grade ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM applications, and scalable data pipelines with observability and MLOps practices for cloud and on-prem environments.

Lead design and delivery of scalable Amazon Connect voice and chat solutions. Build Lambda-based integrations, integrate Lex bots and external systems, optimize routing/queues, ensure high availability and monitoring, lead production support, RCA, and continuous improvement for contact center platforms.

Lead design, build, and operate end-to-end AI/ML-enabled full-stack applications for clinical workflows. Develop Next.js/React frontends, Node.js backends, PostgreSQL data models, integrate LLM capabilities, deploy and monitor on Azure, implement CI/CD and security best practices, and mentor engineers.

Lead a team to measure and improve enterprise search relevance and ranking using ML and LLM/RAG techniques. Manage end-to-end model development, evaluation, deployment, cross-functional collaboration, and team growth to ensure scalable, production-grade search quality.

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.

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.

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 development and productionization of ML and LLM-based solutions for sales coaching. Own end-to-end Python code lifecycle, run experiments, tune models, integrate generative AI, and collaborate with product and business partners to deliver scalable ML systems.

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.

Apply advanced quantitative and computational methods to build and deploy machine learning, optimization, and statistical models on large structured and unstructured consumer data. Drive full modeling lifecycle from problem definition to communication of results and recommendations, present findings to senior stakeholders, and act as a subject-matter expert while contributing new ideas to the team.

Build and validate ML, optimization, and statistical models on large structured and unstructured consumer datasets; generate insights and actionable recommendations; communicate results to senior stakeholders; contribute across the full modeling lifecycle and advance team knowledge.